Introduction
According to the IPCC report , the influence of aerosol
loading on solar irradiance remains a relevant, open question. Aerosol
particles influence the radiative balance of the Earth due to aerosol–radiation
and aerosol–cloud interactions. The aerosol–radiation interactions include
the scattering and absorption of solar radiation (direct effect), and potential
changes in cloud properties associated with these radiative effects
(semi-direct effect). Another important consequence of aerosol–radiation
interactions is an increase in the diffuse fraction of solar radiation
entering the Earth surface.
The influence of aerosol–radiation interactions on the diffuse fraction of
solar radiation is relevant for estimates of the terrestrial carbon sink
and for understanding climate feedback mechanisms e.g..
The increase in the land carbon sink due to enhanced industrial aerosols
was estimated to be 20 %–25 % during the period of “global
dimming” between 1960 and 1999 in the modelling study by . The physical
mechanism behind the growth of the terrestrial carbon sink is as follows. An
increased diffuse fraction of solar irradiance due to aerosols makes it easier
for light photons to penetrate into the canopy. Moreover, diffuse light
coming from different angles increases the efficiency of CO2 uptake by
leaves of different orientation . This leads to an increase
in the light use efficiency (LUE) of plants, which quantifies the amount of CO2
fixed by an ecosystem per unit of photosynthetically active radiation (PAR), and gross
primary production (GPP), which quantifies the amount of CO2 fixed by an
ecosystem per unit area per unit time. However, this mechanism is
ecosystem-dependent and presumably depends on canopy height and the leaf area
index (LAI) : an enhanced diffuse radiation does not
lead to an increase in grasslands' GPP, while GPP of broadleaf forests can be
significantly increased. Similarly, a study based on AmeriFlux data from
several sites , including broadleaf forests and crops, suggested
an increase in GPP due to diffuse radiation for forests but not for
crops.
Estimates of the aerosol effect on GPP are uncertain for two reasons. First,
it is not clear how large the effect of aerosols is on diffuse radiation.
Second, the associated effect of diffuse
radiation on GPP can be both negative and positive . Therefore, aerosol–radiation interactions may either lead to an
increase or a decrease in GPP, depending on aerosol loading. As an example,
reports an increase in GPP for broadleaf forests at any
aerosol loading typical for the sites they considered. On the contrary, a
recent model study suggests that for a substantial proportion of boreal
forests in the territories of Finland, Estonia and Russia, the direct aerosol
effect at relatively high aerosol loading leads to a decrease in the annual
diffuse irradiance and GPP . Estimates on the effects of solar
radiation on GPP have often been obtained by utilizing parameterizations
or based on the results of numerical modelling and satellite
observations . The aims of this study are to provide
a comprehensive data analysis related to the direct effect of aerosol on
solar radiation, to separate cloud and aerosol effects on solar irradiance,
and to further quantify the influence of solar radiation on GPP. The data
sets include continuous field measurements from five stations in Finland,
Estonia and Russia.
Note that this analysis can be put into a broader context regarding the
quantification of terrestrial feedback loops, e.g. the COntinental
Biosphere–Atmosphere–Cloud–Climate (COBACC) feedback loop .
This loop considers the effect of the carbon dioxide concentration on biogenic
volatile organic compound emissions (BVOC), further relates BVOC-aerosol
interactions to the variability in solar radiation and finally the loop is
closed with the effects of radiation for the ecosystem GPP and CO2
concentration. Here we provide a quantification of the part of this loop
related to aerosol–solar radiation–photosynthesis interactions in boreal
and hemiboreal forests.
In line with the aims of this study, we focus on two problems:
(1) aerosol–radiation interactions, for which we quantify the diffuse fraction of
solar radiation that can be observed due to the direct aerosol effect, and
(2) the diffuse radiation effect on photosynthesis. First, we quantify
the effect of aerosol–radiation interactions on diffuse radiation in boreal
forests. For the estimates of the effect of aerosol on solar radiation it is
important to separate clear times of the day from cloudy periods, because clouds
are much more effective than aerosols with respect to scattering solar irradiance. In
some previous studies this separation was made
based on the ratio of measured global irradiance to
theoretically calculated global irradiance at the top of the atmosphere. This criterion of
clear days can indeed be acceptable when one needs to distinguish between
mostly sunny/mostly cloudy days with respect to the incoming global
irradiance or the incoming solar energy. However, it fails to separate
sunny/cloudy periods from the point of view of the amount of diffuse irradiance, which
is crucially important for photosynthesis. Generally, radiation data used for
analyses is averaged over a time interval (often 30 min). Some types
of clouds, such as cumulus, are rather intermittent with
oscillation periods of several minutes e.g.. During the periods
when there is no cloud in front of the sun, global irradiance can be even
higher than theoretically predicted due to a global radiation enhancement
. As a result, an averaged global irradiance can be close to
that theoretically predicted for the clear sky, while averaged diffuse irradiance is
significantly enhanced at the same time due to the presence of
clouds. Such data can be erroneously attributed to clear-sky conditions,
meaning that the effects of clouds on the diffuse radiation can be associated with
the direct aerosol effect. These kinds of conditions are most likely to cause
the highest GPP, as global irradiance does not decrease while diffuse
irradiance is already high. Second, we consider the effect of solar radiation
on GPP. We investigate the effect of the diffuse fraction of solar radiation
on the LUE and further quantify the maximum effect on GPP due to aerosol–radiation
interactions for different ecosystems in a boreal forest.
Materials and methods
In this section we introduce data sets and methods used in the study. In
Sect. we present the sites and data sets. Section
describes the clear-sky model used to separate clear sky and clouds. We
discuss the consequences of having different (PAR or broadband) radiation
measurements at different sites in Sect. and suggest a method to
make the data sets comparable. In Sect. we introduce the
condensation sink as a measure of aerosol loading. Finally, Sect. describes the method used to study the
diffuse radiation effect on ecosystem GPP using a separation of GPP into the
LUE and the PAR.
Data sets from different sites used in this study.
Station
Parameters
Years
SMEAR I
Global and diffuse radiation (PAR),
2015–2016
(67∘46′ N, 29∘36′ E, 390 m a.s.l.)
particle number-size distribution, GPP.
SMEAR II
Global and diffuse radiation (broadband),
2008–2009
(61∘51′ N, 24∘17′ E, 181 m a.s.l.)
particle number-size distribution, GPP.
SMEAR II
Global and diffuse radiation (PAR),
2010, 2014–2015
(61∘51′ N, 24∘17′ E, 181 m a.s.l.)
particle number-size distribution, GPP.
SMEAR Estonia
Global and diffuse radiation (broadband and hyperspectral),
(58∘16′ N, 27∘16′ E, 36 m a.s.l.)
particle number-size distribution, GPP.
2015–2016
Fonovaya
Global radiation (broadband), particle number-size distribution,
2016–2017
(56∘25′ N, 84∘04′ E, 80 m a.s.l.)
CO2 concentration at 10 and 30 m, wind speed,
pressure, air temperature, relative humidity.
Zotino
Global and diffuse radiation (PAR), CO2 flux, air temperature.
2012–2016
(60∘48′ N, 89∘21′ E, 180 m a.s.l.)
Sites and data sets
We used data from five remote forest sites located at the middle and
relatively high latitudes in Finland, Estonia and Russia: SMEAR I (Värriö,
Finland), SMEAR II (Hyytiälä, Finland), SMEAR Estonia (Järvselja,
Estonia), Zotino (Zotino, Krasnoyarsk region, Russia) and Fonovaya (Tomsk
region, Russia). Figure illustrates the sites' locations on the map,
while Table 1 summarizes information on the data sets used in the present
study. The information regarding the instruments used can be found in separate
papers that describe the stations (listed below) and on the AVAA Internet site for SMEAR I and
II (https://avaa.tdata.fi/web/smart, last access: 30 June 2018).
Location of the sites (see Table 1 for latitudes and longitudes of
the different stations).
SMEAR II is located at Hyytiälä Forestry Field Station in a
conifer boreal forest near a lake in central Finland. The site is a managed,
55-year old Scots pine (Pinus sylvestris L.) forest stand with a
closed canopy and an average tree height of ca. 18 m.
SMEAR I is the site characterized by the highest latitude and
is located in Finnish Lapland. The site is comprised of ca. 60-year old Scots pines with a
rather open canopy. The average tree height is ca. 10 m.
SMEAR Estonia is located in a hemiboreal forest zone and the
stands in the tower footprint consist of a mixture of coniferous, Scots pine
and Norway spruce (Picea abies L. Karst), and deciduous, silver
birch (Betula pendula Roth.) and downy birch (Betula pubescens Ehrh.), trees. Because of
the high heterogeneity and the mosaic of stands of hemiboreal forests the
stand age is 65-years old on average ranging from 43-year old larch stands to
120-year old pine stands. The average ages of the dominating species are
102 years for pine, 79 years for spruce and 68 years for birch. Therefore,
the tree height is variable: 22 m on average (ranging between 10 and 30 m).
Fonovaya in the Tomsk region is the most southern site . It is located
on the Ob River in western Siberia, Russia, and forest stand is represented
by mixed forest. The stand is dominated by 50-year old Scots pine, 45-year
old birch (Betula verrucósa) and 27-year old aspen (Populus tremula). The average tree height is ca. 30 m, ranging from 25 m for birch
up to 40 m for pines.
Zotino is located near the Yenisei River in central
Siberia. The forest is dominated by a 100-year old Scots
pine (Pinus sylvestris L.)
forest stand with an open canopy and an average canopy height of ca. 20 m.
Thus, we have data sets from five sites with three of them represented by
pine stands and two represented by mixed forests; all of the sites are
located at mid-latitudes. Note that currently the data from these five
stations form the largest possible set of simultaneous atmospheric
observations on trace gases, meteorology, solar radiation and aerosols,
conducted in boreal and hemiboreal forests in Eurasia. Some of these sites
lack certain components necessary for the analysis and therefore we use
parameterizations when needed (and possible). For example, the diffuse
fraction of solar radiation, fdifbb, was parameterized in the
data set from Fonovaya. Following and , we used
the formula
fdifbb=Rd/Rg=1.45-1.81x,
where Rd is diffuse radiation, Rg is global radiation and x=Rg/RTOA is the ratio of the measured global radiation
to the modelled radiation at the top of atmosphere. For x<0.28 we used
fdifbb=0.95, and for x>0.75 we used fdifbb=0.1.
The radiation at the top of atmosphere was calculated as RTOA=I0cos(sza), where I0 is the solar constant and sza is the solar
zenith angle.
The data used in this study correspond to the peak growing season defined as
the time period with maximum GPP. Typically it includes June–August and
part of May and September for all the sites except SMEAR I, where it includes
July–August and part of June and September. For consistency, we used
June–August data for SMEAR II, SMEAR Estonia, Fonovaya and Zotino and
July–August data for SMEAR I. The analysis was performed for daytime
(09:00–15:00 local time), 30 min averaged data.
The aerosol number-size distribution at Fonovaya was measured using two
instruments, which do not overlap with respect to the size range. Therefore, there is a
gap between 200 and 300 nm in the distribution. This gap was filled with an
average value between the two adjacent points (one point was added between
the two points). Apart from that, we did not apply gap-filling for aerosol
data in this study, and used only good quality data. We did not fill gaps in
the solar radiation data.
Note, that the CO2 flux was obtained by the micrometeorological (eddy
covariance) method at the SMEAR stations and Zotino, while the gradient method
was applied to obtain the CO2 flux from the Fonovaya data set. GPP was calculated
using the following formula:
GPP=TER-NEE,
where TER is the total ecosystem respiration and NEE is the net ecosystem
exchange. TER was obtained for all ecosystems using the nighttime method of
CO2 flux partitioning . The partitioning method at
SMEAR I and II was based on soil temperature which makes the method more
precise , while at other sites it was based on air
temperature (soil temperature is not measured). According to ,
a possible consequence of an average GPP calculated during daytime
(09:00–15:00), is a decrease in GPP calculated using soil temperature of
about 0.5 µmol s-1 m-2 compared with GPP calculated
using air temperature. In addition, in the data sets from the SMEAR stations
absent GPP points were modelled , while in the data sets from
the Russian stations we did not fill the gaps. For the Zotino data set a good data
percentage was 55 % on average , and for the Fonovaya data set it was
approximately 30%.
Studying aerosol–radiation interactions, we used the Solis clear-sky model (see
more in Sect. 2.2). The input parameters for this model are the aerosol optical
depth at 700 nm (AOD700) and precipitable water (PW). We used AOD at
675 nm and PW from AERONET ; in particular, data from the
following AERONET sites were used: Hyytiala (for SMEAR II), Sodankyla (for SMEAR I),
Tomsk22 (for Fonovaya) and Toravere (for SMEAR Estonia). The AERONET sites are in
the immediate vicinity of the Fonovaya and SMEAR II stations, SMEAR Estonia is 50 km
from Toravere and SMEAR I is approximately 70 km from Sodankyla.
We used Version 2, Level 2 data (cloud screened and quality controlled),
except for Tomsk22 where we used Version 3, Level 2 data. The input data were
averaged over daytime.
Currently aerosol characteristics are not measured at Zotino and there are no
AERONET sites nearby; therefore aerosol–radiation interactions have not been
studied for Zotino. However, radiation and CO2 flux, which are necessary for the
radiation–photosynthesis analysis, are measured; consequently, the Zotino data set has
been included in this study.
The Solis clear-sky model
To distinguish between the effects of aerosols and clouds on solar radiation,
we used a simplified broadband version of a clear-sky radiative transfer
model (RTM) – Solis . This is a quasi-physical model which
means that it employs Lambert–Beer relations for the general estimates of
irradiance while using parameterizations for the total optical depths. The
input parameters are the aerosol optical depth at 700 nm (AOD700) and
precipitable water (PW). Parameterizations are developed for the following
range of parameters: sea level < altitude < 7000 m,
0 < AOD700 < 0.45 and 0.2 cm < PW < 10 cm.
Solis parameterizations were obtained using the “urban” type of aerosol size
distribution in the full RTM . The difference between
calculations for “urban” and “rural” types of aerosol for the same
AOD700=0.18 was reported by . This AOD700 value
is larger than the typical values for all of the places that we
consider in this study; hence, we expect smaller errors due to the
inconsistent aerosol type. The difference reported by was
negligible for direct irradiance, whereas global irradiance for “rural”
aerosol was around 20 W m-2 larger during the
daytime. Given that for clear-sky conditions between 09:00 and 15:00 and
during the growing season, global irradiance drops to ∼600 W m-2, this difference introduces a maximum error of 3 %.
More tests for several sites in the USA and comparison between Solis and two
more simplified clear-sky models, Bird and REST2, were reported by
. Solis is the optimal model from the point of view of the
required parameters: it performs only slightly worse than the
other two models, and it requires only two input parameters.
We used Solis to model both global and diffuse irradiance. The horizontal
global irradiance, Igh, was calculated as follows:
Igh=Rg,mod=I0′exp-τgcosg(sza)cos(sza),
while the direct irradiance, Idir, was calculated as
Idir=I0′exp-τbcosb(sza).
Here I0′ is a common modified (enhanced) irradiance defined in
, τb and τg are the direct and global total
optical depths, respectively, b and g are the fitting parameters, and sza is the solar
zenith angle. Diffuse radiation can be found from the global radiation
balance using the following equation:
Idh=Rd,mod=Igh-Idircos(sza).
All of the parameterizations used in the model are given in . The
algorithm used for the calculations is written in Fortran. For the calculation
of the sza, the online calculator Solar Position Algorithm (SPA) was used
(available from http://www.nrel.gov, last access: 22 February 2018).
Accounting for the difference between PAR and broadband radiation
Note that the measurement methods of solar radiation are different at the
different sites. At SMEAR II before 2009 and Fonovaya only broadband
radiation is measured, while at SMEAR I and Zotino only photosynthetically
active radiation (PAR) is measured. At SMEAR II after 2009 both global PAR
and broadband global radiation, as well as diffuse PAR are measured.
Broadband shortwave radiation, referred to as broadband radiation, is the
radiation in the spectral range between 0.3 and 4.8 µm, while PAR
is the radiation relevant for photosynthesis, i.e. in the range of
wavelengths between 400 and 700 nm. The former is typically measured using
thermopile pyranometers (energy sensors) and reported in energy units
(W m-2), while the latter is measured using quantum sensors and is
reported in quantum units (µmol s-1 m-2).In the
following, we quantify the ratio between global PAR and global broadband
radiation, and the ratio between the diffuse fraction of PAR and the diffuse
fraction of broadband radiation.
Following , the ratio of PAR
(µmol s-1 m-2) to the broadband radiation (W m-2)
is called the PAR quantum efficiency χ. By dividing global PAR by broadband
global radiation at SMEAR II and finding its mean value (the values were
obtained for the growing season and years listed in Table 1), χglob=2.06 µmol s-1 W-1 was obtained, and was somewhat higher than
1.81 µmol s-1 W-1 reported by for
Estonia.
We explain the potential difference between the diffuse fraction of PAR and
the diffuse fraction of broadband radiation as follows. Aerosol particles
influence a certain part of the spectra of solar irradiance depending on the
particle size distribution e.g.. This effect can be better
understood using a dimensionless size parameter πdp/λ,
where λ is the wavelength of incident irradiance and dp
is the particle diameter. If πdp/λ≪1, then Rayleigh
scattering is prevailing, while πdp/λ≫1 means that
the scattering properties of the particles are determined by the geometrical
optics, i.e., so-called “geometric scattering”. The characteristics of the
aerosol distribution become important for solar irradiance if πdp/λ∼1. For boreal forests where the particle size
distribution is typically governed by the well-pronounced mode with the
geometric mean diameter dp≈100 nm , the
effective interaction of aerosols with solar radiation occurs in the
ultraviolet range (100–400 nm) and in the blue part of the optical spectrum
(400–500 nm). Compared to PAR (400–700 nm), the essential part of the
broadband radiation energy is contained in the near infrared part of the
spectrum (700–1400 nm), which is not affected by the relatively small
particles prevailing in aerosol distributions typical for boreal forest. In
other words, the effect of aerosols on the diffuse fraction of solar
irradiance can be different for PAR and broadband radiation. Qualitatively,
one would expect that both the increase in diffuse radiation and the decrease
in global radiation would be more pronounced for PAR, meaning that the
diffuse fraction of PAR would be higher under the same aerosol conditions. In
order to make our analysis consistent and to be able to compare results from
different sites, we performed an analysis of the data from SMEAR Estonia to
compare the diffuse fractions of PAR and broadband radiation. The data set
includes 4 years, from 2014 to 2017.
The measurements at SMEAR Estonia are made using an energy sensor. The
hyperspectral radiometer SkySpec is a purpose-built
instrument for the automated measurement of global and diffuse sky irradiance. To
obtain PAR radiation, the spectral data are converted from energy units to
quantum units and are integrated over a 400–700 nm spectral range. Integration
over the whole available spectral range in energy units is used for
simulating a thermopile pyranometer.
The condensation sink as a measure of aerosol loading
As previously mentioned in Sect. 1, this study can be considered as a part
of the project quantifying the COBACC feedback loop using ground-based
measurements. The condensation sink (CS) is a typical parameter calculated from a
ground-based aerosol number-size distribution and characterizing aerosol
loading. It can be related to measured organic vapours, making it possible to
study the effect of the formation and growth of secondary aerosol for
photosynthesis. In addition, the CS was chosen in the original study of the COBACC
feedback loop by ; therefore, for the sake of comparison we resort to this
parameter.
The CS is calculated from the particle number-size distribution as follows:
:
CS=2πDv∫0dp,maxdpβn(dp)ddp,
where Dv is the diffusion coefficient of the condensing vapour,
n(dp) is the particle number-size distribution, and β is
the Fuchs–Sutugin coefficient. The physical meaning of the CS is the inverse
time needed for vapours to be taken up by existing aerosol particles. Similar
to the scattering coefficient and the AOD, the value of the CS depends on
aerosol surface area. However, the contribution of large particles to the CS diminishes with
increasing particles diameter, in contrast to the aerosol surface
area. This effect becomes pronounced for particles with a diameter larger than
about 300 nm. For boreal forests, where the mode with a geometric mean
diameter of around 100 nm dominates the particle number-size distribution,
the CS can be assumed to be directly proportional to the aerosol surface area
. Thus, one can assume that the CS is an appropriate
measure of atmospheric aerosols for the radiation studies in boreal forests.
The connection between this parameter and AOD500 for boreal forests is
discussed in more detail in Appendix A.
The LUE and PAR analysis to assess the effect of diffuse radiation on GPP
There is a strong evidence that GPP dependence on Rd/Rg
is non-linear and has a maximum ; however, this maximum
is not always well pronounced. In what follows we explain the GPP maximum
based on the ecosystem LUE and PAR dependences on
Rd/Rg. Following , we defined the LUE as the GPP
per unit PAR, therefore GPP=LUE⋅PAR. Strictly
speaking, the LUE is defined as the GPP per unit absorbed PAR, i.e.
PARabs=fAPAR⋅PAR, where
fAPAR is the fraction of the absorbed PAR. The fraction of the absorbed
PAR depends on the leaf area index (LAI), the solar zenith angle and other factors.
This dependence for boreal forests was studied in and . Based
on results reported by , fAPAR
for tree heights larger than 10 m
and at a moderate zenith angle (40–60∘) can be estimated to be
between 0.8 and 0.9. One can obtain the LUE defined by the absorbed PAR by
dividing the LUE used in the present study by 0.8–0.9.
For all ecosystems with a sufficiently deep canopy and a high leaf area index,
the LUE is expected to increase with Rd/Rg, as a larger
fraction of available photons can penetrate inside the canopy and can be
used for photosynthesis. Some studies e.g. have reported a
linearly growing dependence of the LUE on Rd/Rg.
Furthermore, a decrease of PAR with Rd/Rg can be
expected, as an increase in the diffuse fraction of global irradiance
corresponds to the enhancement of the scattering and reflecting properties of
the atmosphere due to the presence of aerosols or clouds. Therefore, for each
site the dependence of the LUE and PAR on Rd/Rg was
investigated separately, after which the GPP dependence on
Rd/Rg was derived from these two dependences.
Again, in order to have consistent data sets, we recalculated
Rd/Rg obtained from the PAR measurements so that it existed in
terms of broadband radiation at all the sites. Conversely, broadband global
radiation from Fonovaya and part of the SMEAR II data set was recalculated to
PAR when investigating the LUE and PAR dependence on Rd/Rg.
We multiplied the global radiation by χglob=2.06 µmol s-1 W-1 in order to get PAR in quantum
units. The PAR quantum efficiency was chosen to be equal to the one at SMEAR II,
as for the daytime and similar solar zenith angles it is mostly aerosol
dependent; SMEAR II and Fonovaya generally have similar aerosol loading
values which can be confirmed by their similar CS values (Fig. ).
Considering the LUE, we filter out the data with low global irradiance,
Rg⩽100 W m-2
(PAR ⩽ 200 µmol s-1 m-2). Below this
critical Rg, the LUE shows significant scatter (it is high for the
low radiation values); therefore we excluded these data from analysis.
Results and discussion
We present the results of our study in two subsections. In Sect. 3.1 we
report the results related to the aerosol effect on solar radiation, and in
Sect. 3.2 we report the results related to the effect of diffuse radiation
for ecosystem photosynthesis. The link between these results and their
relation to other studies are discussed in Sect. 3.3.
Modelled vs. measured irradiance for SMEAR Estonia: measured global
radiation, Rg,meas; modelled global radiation,
Rg,mod; measured diffuse radiation, Rd,meas; and modelled diffuse
radiation, Rd,mod. (a) 1 June 2016 – clear day; (b) 6 June 2016 – cloudy day.
Timescale corresponds to local winter time
(UTC+2).
Aerosol effect on solar radiation
Criterion of clear sky based on the Solis clear-sky model
To understand the importance of the clear-sky criterion for the diffuse
fraction of global radiation, we report the model test against diffuse and
global irradiance measurements at SMEAR Estonia ( the Solis clear-sky model is
described in Sect. 2.2). Examples of the global and diffuse radiation diurnal
cycles for clear and cloudy days are displayed in Fig. . Note
that on a cloudy day with patchy clouds, the AOD can still be measured. Model
results for a cloudy day report the global and diffuse clear-sky radiation for
AOD and PW measured on that day. In general, the model performs well during
clear-sky conditions as can be seen in Fig. a.
This is in accordance with the results of who reported good
performance of the model for clear-sky conditions at several sites in the USA.
On a cloudy day (Fig. b), there were times (e.g. at
09:00 and 17:00) when the measured and modelled global irradiance
(Rg,meas and Rg,mod) were nearly equal while the
measured diffuse irradiance (Rd,meas) was significantly higher
than the modelled one (Rd,mod). The criterion of clear sky based
on the comparison between the modelled and measured global irradiance, i.e.
involving only global irradiance e.g., does not filter
out these points.
The modelled vs. measured diffuse fraction of global irradiance (SMEAR
Estonia, 2016). Closed symbols represent the criterion of clear sky based on diffuse and
global irradiance (Rg,meas/Rg,mod>0.9,
Rd,meas/Rd,mod>0.8), the closed symbols and the open symbols combined represent the criterion of clear sky based on global irradiance
(Rg,meas/Rg,mod>0.9). The solid line
illustrates the ideal 1:1 ratio between the modelled and measured data
sets.
A further illustration of the simplified criterion and its consequences for
the diffuse fraction of global irradiance is given in Fig. ,
displaying the diffuse fraction of global irradiance under clear-sky
conditions at SMEAR Estonia (summer 2016). The open symbols in combination with the
closed symbols correspond to the data obtained using the clear-sky criterion
involving global radiation alone:
Rg,meas/Rg,mod≥0.9.
The closed symbols, in comparison, show the data obtained with the criterion involving
both the diffuse and global radiation:
Rg,meas/Rg,mod≥0.9and0.8≤Rd,meas/Rd,mod≤1.2.
The criterion of clear sky suggested here is based on the results of
, who determined a rms error of the linear regression
corresponding to the measured global radiation and modelled global radiation using Solis
for eight sites. This rms error did not exceed 10 % (cf.
Equation using global radiation), and was generally lower than
that. As for diffuse radiation, we chose a 20 % difference between measured
and modelled diffuse radiation in Equation (). This difference
was chosen based on the estimated 18 W m-2 error in diffuse radiation
between the full Solis radiative transfer model and measurements, reported by
, by assuming a typical diffuse radiation value of
120 W m-2, and by adding a 5 % error between the simplified model
and the full Solis radiative transfer model. The closed symbols in
Fig. show a good agreement between the measured and modelled
diffuse fractions of solar irradiance Rd/Rg, while a
considerable portion of the open symbols has large measured
Rd/Rg – meaning that they contain a large fraction of
cloud-influenced data. Therefore, the criterion of clear sky based on the
global and diffuse radiation can be used to detect clear-sky data when it is
important to separate the effect of aerosol and clouds on diffuse radiation.
Note that the diffuse fraction of solar irradiance due to aerosol loading at
SMEAR Estonia lies between 0.08 and 0.21. As shown later, this relatively low
ratio pertains to all the sites in this study: the maximum diffuse
fraction of solar irradiance due to the direct aerosol effect was no more
than 0.27 at the remote sites in boreal forests during the growing season.
Ratio fdifPAR/fdifbb as a function of
fdifbb (SMEAR Estonia). Different curves correspond to the best
fits of the data for different solar zenith angles (sza).
Parameterization of the diffuse fraction of PAR as a function of the diffuse fraction of broadband radiation
In this section we discuss the difference between the diffuse fractions of
PAR and broadband radiation. Figure displays the ratio between
the diffuse fraction of PAR, fdifPAR, and the diffuse fraction
of broadband radiation,fdifbb, as a function of
fdifbb where fdif=Rd/Rg. Since the
radiation level depends on the solar zenith angle, we cast the daytime data
into three solar zenith angle bins; the width of each bin was 10∘.
Each data set was fitted by the exponential function
fdifPARfdifbb=aexp(-(fdifbb-b)/c)+d.
The coefficients of the fitting function for each bin are reported in
Table 2. Using this function, we can compare the diffuse fractions of PAR and
broadband radiation over the whole range of sky conditions, including clear
and cloudy skies. As expected (see more in Sect. ), in
Fig. we observe an increase in the diffuse fraction of PAR up to
27 % compared with the value for broadband radiation at small
fdifbb, corresponding to clear-sky conditions. In absolute
values, this difference between the diffuse fraction of the PAR and broadband
radiation is not very large (e.g. fdifbb=0.15 corresponds to
fdifPAR=0.18 under clear-sky conditions). However, as the
diffuse fraction of global radiation in boreal forests varies in a relatively
small range due to the direct effect of aerosol (e.g. Fig. ), it is
important to make corrections. As can be further noted from
Fig. , the ratio fdifPAR/fdifbb
approaches one for overcast cloudy conditions, as in this case diffuse
radiation prevails for both PAR and broadband radiation.
Best fit parameters for the diffuse fractions ratio,
fdifPARfdifbb, as a function of the
diffuse fraction of broadband radiation, fdifbb, for various
solar zenith angles (Eq. 7).
Solar zenith angle
a
b
c
d
35∘<sza<45∘
0.186
0.140
0.318
0.990
45∘<sza<55∘
0.191
0.146
0.296
0.990
55∘<sza<65∘
0.143
0.351
0.346
0.980
We use these results to obtain the diffuse fraction of global broadband
radiation for the sites where only PAR was measured. In the following we use
the term “diffuse fraction of global radiation” for broadband radiation.
Best fit parameters, correlation coefficients and p-values for
radiation data (Rd/Rg=kCS+b).
Station
kmod, s
bmod
Rmod
pmod
kmeas, s
bmeas
Rmeas
pmeas
SMEAR I
8.30
0.108
0.53
<0.001
6.73
0.176
0.18
0.0422
SMEAR II
10.21
0.092
0.69
<0.001
11.59
0.153
0.33
<0.001
SMEAR Estonia
6.39
0.094
0.60
<0.001
5.50
0.123
0.23
<0.001
Fonovaya
3.32
0.113
0.44
<0.001
–
–
–
–
The diffuse fraction of global irradiance as a function of CS (clear-sky data).
The red symbols represent calculations with the clear-sky model, and blue
symbols represent measurements.
Aerosol influence on the diffuse fraction of global irradiance: comparative analysis for four sites
In this section, we consider the effect of aerosol on the diffuse fraction of
global irradiance. In the following analysis the data were filtered to
include only clear-sky conditions, based on the modelled and measured global
irradiance, using Equation () for all
of the sites. We deliberately used the equation based only on the global
irradiance in order to demonstrate the effect of unfiltered
cloud-contaminated data on the diffuse fraction of solar radiation.
Figure displays Rd/Rg vs. CS at SMEAR I and
II, SMEAR Estonia and Fonovaya (no aerosol data are available from Zotino).
To separate the effect of clouds and aerosol particles, we report two
quantities: the measured diffuse fraction of global irradiance (Fig. 5, blue
symbols) and the modelled diffuse fraction of global irradiance (Fig. 5,
orange symbols). Based on the analysis in Sect. 3.1.1, modelling provides
information about the direct effect of aerosols on the diffuse fraction,
while measurements illustrate the combined effects due to aerosols and
clouds. Note that for consistency all the ratios Rd/Rg
were corrected in accordance with the previous section, meaning that only the
ratios corresponding to the broadband radiation are reported (although PAR is
measured at SMEAR I and II). For SMEAR I, the model data set includes
4 years, while only 2 years of measured data are available. Diffuse radiation
is not measured at Fonovaya station; hence, only model results are shown.
Moreover, the data from 2016 were not used due to forest fires in Siberia.
Smoke plumes have large influences on the aerosol size distribution as a
result of which the clear-sky model fails to predict diffuse and global
radiation.
An increase in Rd/Rg with increasing CS is observed at
all of the sites, as follows from the model results (also representative for the
measurements with an appropriate clear-sky equation, as discussed in
Sect. and demonstrated in Fig. ). Note that the
modelled values of Rd/Rg correspond to the lower points
in the measured data sets. The blue points above the modelled data are
characterized by a larger diffuse radiation than those obtained for current
AODs using Solis; hence, they represent the effect of clouds. According to the
model calculations, the maximum diffuse fraction of global radiation due to
the direct effect of aerosols did not exceed 0.27, while the minimum fraction
was about 0.1. Furthermore, the aerosol population with CS smaller than approximately
0.005 s-1 do not significantly contribute to light scattering
(Fig. ), as the diffuse fraction of global irradiance for these
values of CS is almost constant and close to 0.1; this can be generally attributed
to Rayleigh scattering.
We fitted modelled and measured data with the linear function
fdifbb=kCS+b. The best-fit coefficients and
correlation coefficients for four sites are reported in Table 3. All of the
dependences pertaining to the modelled data, i.e. to the direct effect of
aerosol particles on solar radiation, had correlation coefficients larger
than 0.5 corresponding to a moderate correlation (except Fonovaya, where R=0.44, which was most likely due to the small data set). On the contrary,
cloud-influenced data demonstrate rather weak correlations with 0.18<R<0.33.
Light use efficiency (LUE) as a function of the diffuse fraction of
global irradiance (Rd/Rg). All dependences are
statistically significant (p<0.001).
The effect of diffuse radiation on GPP
The effect of diffuse radiation on GPP: comparative analysis for all of the sites
In this section we study the LUE and PAR dependences on the diffuse fraction of
global radiation in order to better understand the behaviour of the dependence of GPP
on Rd/Rg. Figure displays the
dependences of the LUE on Rd/Rg for all sites. All of these
dependences exhibit a linear relationship with the correlation coefficients
between R=0.67 and R=0.83 (except Fonovaya with R=0.44, which can
be attributed to both the short data set and the less precise gradient method used
for the CO2 flux calculations). The LUE slope reflects the canopy
properties, i.e. it characterizes the ability of a forest stand to take up
more CO2 in response to an increasing diffuse fraction of solar
irradiance. The steepest LUE slopes pertain to the mixed forests at SMEAR
Estonia and Fonovaya, while the slopes are approximately 60 % less steep
in coniferous forests (Table 4). This difference is presumably due to the
forest type, as mixed forests have a larger potential for photosynthetic
activity enhancement due to a larger leaf area index and a deeper canopy. We
emphasize that the difference is seen in the LUE, in accordance with the LUE
definition given in Sect. , which includes a dependence on LAI
and tree height attributed to fAPAR in the standard definition. Note that
the increase in the LUE from approximately 0.01 to 0.03–0.04 mol
CO2 mol photons-1 observed for mixed forests is similar to
that reported by for mixed and broadleaf forests in the
USA.
Linear regression coefficients for PAR and LUE at different sites:
LUE=L1+L2⋅(Rd/Rg), PAR=R1+R2⋅(Rd/Rg).
Station
L2, molCO2molphotons
L1, molCO2molphotons
R2, µmol s-1 m-2
R1, µmol s-1 m-2
SMEAR I
0.0157
0.0062
-944
1212
SMEAR II
0.0164
0.0098
-1081
1480
SMEAR Estonia
0.0278
0.0094
-1194
1608
Fonovaya
0.0238
0.0092
-1085
1575
Zotino
0.0143
0.0058
-1118
1548
Figure displays the dependences of the global PAR on
Rd/Rg for all sites. As expected, PAR decreases as
Rd/Rg increases: at smaller values due to aerosol
particles, and at larger values due to clouds. As follows from Fig. 5, the
values of Rd/Rg<(0.2-0.27) mostly correspond to the
influence of aerosol particles (but they can also be influenced by thin
clouds), while larger values of Rd/Rg are associated
with the presence of clouds. Similarly to the LUE, these dependences are linear
with high correlation coefficients (0.78<R<0.90). Generally, the slopes
of the linear dependences in Fig. were similar (within the range of
1081–1194 µmol s-1 m-2), which can probably be
attributed to similar cloud attenuating properties over all of the sites at
middle latitudes. The exception is SMEAR I, where the slope is lower
(944 µmol s-1 m-2). Solar radiation under clear-sky
conditions is also significantly lower at SMEAR I compared with the other
sites, which is partly due to the high latitude, and partly because the
growing season at SMEAR I is July and August (i.e. it does not include June
which has the highest global irradiance values).
Photosynthetically active global radiation (PAR) as a function of
the diffuse fraction of global irradiance (Rd/Rg). All
dependences are statistically significant (p<0.001). The number of sample
points is reported in the caption of Fig. .
The vertical scattering of the data in Fig. is presumably due to
two factors: first, the variability in the radiation intensity during
daytime and growing season, and second, the different influences of
clouds, as the same diffuse fraction of global irradiance may pertain to the
different attenuations of global radiation by clouds. Note that the latter
factor is excluded from the Fonovaya data set by the parameterization. One
can conclude that the PAR variability due to clouds was larger than the
diurnal (associated with different solar zenith angles during the day) and
day-to-day PAR variability in the growing season. Thus, additional binning
by, e.g. solar zenith angle, would be redundant, as the decrease in PAR
variability due to binning would be hidden by the stronger scattering due to
clouds.
Finally, based on the linear dependences of the LUE on
Rd/Rg and PAR on Rd/Rg, we can
estimate how GPP depends on Rd/Rg. When we multiplied
the LUE by PAR, parabolic dependences were obtained for all the sites, with a
maximum due to the effect of diffuse radiation on photosynthesis.
Figure shows the estimated GPP dependences on Rd/Rg
for the different sites for comparison, while Fig. displays data
sets and estimated curves separately for all sites, similar to
Figs. and .
Estimated GPP dependences on Rd/Rg for all of the
sites (obtained as GPP=LUE⋅PAR using the
coefficients for PAR and the LUE dependences on Rd/Rg
reported in Table 4).
GPP dependences on Rd/Rg for all of the sites.
The curves represent estimated GPP (the same parabolas as in Fig. ).
We use a dashed curve for Fonovaya due to the relatively low correlation
coefficient obtained for the LUE (R=0.44, Fig. ). The data sets for
all of sites were cast in bins in Rd/Rg, the width of each bin
is Rd/Rg=0.04 (Rd/Rg=0.08 for
Fonovaya). Black points correspond to the mean GPP in each bin and error bars
show the standard deviation of the data for each bin.
Constraints on the LUE and the diffuse fraction of solar radiation associated with the maximum ecosystem GPP under diffuse light
Well-pronounced linear dependences of the LUE and PAR on
Rd/Rg can be used to estimate how large an increase in
the LUE should be in order to have GPP increase under diffuse radiation and at
what diffuse fraction of solar radiation the maximum GPP can be observed. If
LUE=L1+L2⋅(Rd/Rg),PAR=R1+R2⋅(Rd/Rg),
then the maximum GPP is reached at (Rd/Rg)max=-0.5(L1/L2+R1/R2), estimated as the point where the parabola
GPP=LUE⋅PAR has its maximum. The position of this
maximum depends on the ratios L2/L1 and R2/R1. For a certain range of
parameters, the maximum of the parabola can be located at
Rd/Rg<0.08, which is below the minimum diffuse
fraction measured at our sites; therefore, it is not feasible for the
latitudes we consider in this study. In this case, GPP monotonically
decreases when Rd/Rg increases from ∼0.1 to 1.
Conversely, (Rd/Rg)max should be larger than
0.08–0.1 for the GPP to have a maximum under diffuse light. Note, that PAR
dependences on Rd/Rg at middle latitudes are similar:
R1/R2≈-1.5, while for SMEAR I this ratio is R1/R2≈-1.3. From these estimates, L2>L1/1.2 for the middle latitudes. Since
L1 is roughly the minimum value of the LUE at Rd/Rg≈0.1 (clear sky), while L1+L2 is the maximum of the LUE at
Rd/Rg=1 (overcast conditions), L2 can be treated
as the maximal gain in the LUE under diffuse light. Thus, the GPP will have a
maximum associated with diffuse radiation if the ecosystem LUE under diffuse
light increases by more than approximately 80 % of its minimum possible
value (which is observed under clean conditions on clear days). For the sites
considered in the present study, the smallest gain in the LUE due to diffuse
radiation is observed at SMEAR II, where the LUE under diffuse light was
almost twice as large as its value on clear days. The largest gain was at
SMEAR Estonia and Fonovaya where the LUE grew by almost a factor of 3 if the
dominating radiation conditions in the area changed from mostly direct to
mostly diffuse radiation. Therefore, all ecosystems displayed maxima of GPP
dependence on Rd/Rg due to diffuse light, although at
different values of Rd/Rg.
Moreover, this approach clearly demonstrates that the maximum GPP can never
be reached under overcast conditions. If we again take R1/R2=-1.4 as
for the middle latitudes, then the position of the maximum is at
(Rd/Rg)max=-1/2(L1/L2)+0.7. One can
immediately deduce that for large slopes of the LUE, i.e. when L1/L2
approaches zero, (Rd/Rg)max approaches 0.7.
At SMEAR I, this position is restricted by
(Rd/Rg)max ≈ 0.65. The maximum
of GPP parabolas for the five sites considered in this study is at
(Rd/Rg)max≈0.4–0.5.
Discussion
In this section we combine the results from the previous sections to make
conclusions regarding the direct effect of aerosols on GPP, and we compare the
results obtained with those from previous studies.
As previously mentioned in Sect. , a cloud-biased data set and
a standard linear regression analysis results in weak but significant
(p<0.001) correlations between CS and Rd/Rg
(Table 3). The relatively high cloud-biased diffuse fraction of global
radiation at low CS leads to an underestimation of the effect of increasing
aerosol loading for the cloud-biased data set. If CS increases from 0.002 to
0.015 s-1 (obtained for the clear-sky conditions), a relative increase
in the diffuse fraction of global radiation following from the clear-sky
model is from 110 % to 165 % at all of the sites except Fonovaya, while
this increase is between 65 % and 118 % for cloud-biased data. In
the following we use only the results for the clear-sky model (representing the
measured data set when the stricter Equation () of clear sky is
applied, as follows from Fig. ). In absolute values the increase
was quite small: from 0.11 to ∼0.27 at SMEAR I and II, and from 0.11 to
∼0.2 at SMEAR Estonia and Fonovaya. The increases in
Rd/Rg over these value ranges led to increases in GPP
from 17.2 to 18.6 µmol s-1 m-2 at Fonovaya, from 18.5
to 20.9 µmol s-1 m-2 at SMEAR Estonia, from 15.8 to
16.9 µmol s-1 m-2 at SMEAR II and from 8.8 to
10.0 µmol s-1 m-2 at SMEAR I. The largest relative
increases in GPP due to the increasing aerosol loading from its minimum value
to its maximum value were observed for SMEAR I and SMEAR Estonia (14 % and
13 % respectively). Note, however, that the median value of CS should be
increased by a factor of about 5 at SMEAR I to get this maximum gain in GPP,
whereas this same increase in GPP would be observed at SMEAR Estonia if the
median CS increased by a factor of 2–3.
Overall, we obtained rather weak dependence of the diffuse fraction on CS. It
is much weaker than that reported by : for all of the sites
the slope is less than 10 s (Table 3) compared with the almost 100 s
obtained in the above-mentioned study. This difference is due to the
inappropriate equation of clear sky selecting cloud-biased points with a
diffuse fraction up to 0.8 and a different statistical method
(bivariate fitting compared with the linear regression used in this study).
Note that reported minimum and maximum possible slopes for an
increase in the diffuse fraction of global radiation with CS. Our present
results are close to their minimum slope. Furthermore, due to the large
diffuse fractions attributed to the effect of aerosols rather than clouds,
the maximum direct effect of aerosols on GPP was overestimated by
. In the present study we obtained a 6 % increase in GPP
at SMEAR II due to the diffuse radiation effect rather than the ≈30% reported by . However, their minimum slope, reported
for GPP vs. Rd/Rg dependence, would result in an
increase of GPP similar to this study.
Note that the aerosol loading observed at all sites corresponds to
0.04 < AOD675<0.35 with the typical values being in the range of
0.05–0.10 and AOD500<0.25 (see Appendix A). In accordance with the
study by , an increase in the diffuse fraction did not exceed 0.3
for these relatively low AOD values. Much higher diffuse fractions (0.5–0.7)
due to the direct aerosol effect were obtained by for the
biomass burning season in the Amazon.
Next, all of the GPP dependences have a maximum due to clouds. The maximum
corresponds to the clouds with a diffuse fraction in the order of 0.4–0.5.
According to and , this
Rd/Rg corresponds to optically thin clouds with cloud
optical thicknesses less than 5. Conversely, GPP decreases for optically
thick clouds, which has also been demonstrated by . The
largest increase is 32 %–33 % at SMEAR Estonia and Fonovaya, whereas
the smallest increase is 11 % at SMEAR II compared with the GPP values
on clear days characterized by low aerosol loading. At middle latitudes
with a similar attenuation of radiation due to aerosols and clouds, the
increase in GPP depends on the LUE slope: the steeper the LUE slope is, the
more pronounced the maximum.
Based on Fig. , similar forest stands at Zotino and SMEAR
I demonstrated a similar dependence of the GPP on Rd/Rg,
while this dependence was different for the coniferous forest at SMEAR II.
GPP at SMEAR II under clear sky is almost 1.5 times larger than the
corresponding GPP at SMEAR I and Zotino, but GPP increase under cloudy sky is
smaller at SMEAR II. This could be a consequence of the closed canopy and the
higher leaf area index of the SMEAR II forest stand. Our GPP data sets,
reported in Fig. , look similar to those reported by Alton et
al. (2007) and Alton (2008). The GPP dependence reported for SMEAR II is also
similar to that reported by Alton (2008) for needle-leaf forests, but for
mixed forests we obtained an increase of up to 30 % compared with the moderate
10 % increase for broadleaf forests reported by Alton (2008). Note that
Alton (2008) used parameterization, Eq. (), for the diffuse fraction of
global radiation, while we had measurements of diffuse radiation at four sites
out of five.
Finally, we considered the data from Zotino including the periods of forest
fires . Figure suggests that forest fires do not have
any specific influence on the PAR decrease with increasing
Rd/Rg compared with cloudy sky. In other words, plumes
from forest fires lead to a similar decrease in PAR and a similar separation in
diffuse and direct fractions as some clouds. The same holds for the LUE of
an ecosystem: the dependence of the LUE on Rd/Rg at Zotino
is similar to that of other coniferous sites. However, a significant increase
in GPP under wildfire plumes can potentially be obtained at a daily timescale
because the radiation regime with
(Rd/Rg)max, i.e. close to the optimal
conditions for ecosystem photosynthesis, can persist for a long time under
plume conditions. At the same time, clouds may be intermittent and the effect of
a sporadic GPP increase can be compensated for by the smaller GPP when clouds are
in front of the sun and radiation is reduced .
Conclusions
We quantified the direct effect of aerosol on solar radiation and GPP in
boreal and hemiboreal forests in Eurasia. The analysis was based on data
from five sites including coniferous and mixed forest ecosystems.
The diffuse fraction of global radiation due to the direct aerosol effect was
estimated to be in the range of 0.11<Rd/Rg<0.27 at
all of the sites.
For the first time we demonstrated a connection between solar radiation
properties (the diffuse fraction of global radiation) and condensation sink.
The latter parameter is used in aerosol studies and it is obtained from
ground-based observations. Employing CS instead of a column-averaged aerosol
parameter AOD is a necessary step towards further investigation of the COBACC
climate feedback loop, linking biogenic volatile organic compounds emissions
and aerosol characteristics.
The GPP-radiation analysis was performed using the separation of GPP into the LUE
and PAR. We found a linear dependence between the diffuse fraction of solar
radiation and the LUE, as well as between the diffuse fraction of solar radiation
and PAR, for all of the sites. While the PAR dependences were quite similar to
one another (except for SMEAR I which is located at a relatively high latitude), the LUE
dependences were different: the slopes were 60 % steeper for mixed forests
than for coniferous forests, and the intercepts were about 40 % lower for
coniferous forests with open canopies. We obtained a parabolic shape for the
GPP dependence on the diffuse fraction of solar radiation. The maximum of the
parabola was more pronounced for mixed forests due to the above-mentioned
differences in the LUE dependences between the mixed and coniferous forests.
Note that parabolic, or near parabolic, shapes have been reported for
different forest sites by and using different
methods to those used in this study.
We showed that GPP can be increased by 6 %–14 % due to the direct
effect of aerosol particles at remote sites compared to clean conditions
with low values of CS. The maximum increase was observed for mixed forests at
mid-latitudes and for coniferous forests at relatively high latitudes.
Furthermore, based on the similarity in the PAR dependences on the diffuse
fraction of solar radiation for all of the sites, we obtained the constraints
on the ecosystems' LUE increase under diffuse light necessary for a GPP
maximum due to diffuse light. At mid-latitudes, the LUE of an ecosystem
should increase by more than ∼80% under diffuse light compared with
its value under clear-sky conditions. Moreover, at the mid-latitude sites,
the diffuse fraction of solar radiation corresponding to the maximum GPP can
not exceed 0.7.
The specific shape of the GPP dependence on the diffuse fraction of solar
radiation suggests that clouds with a 30 min-averaged fraction
Rd/Rg between 0.4 and 0.5 play an important role in
determining ecosystems' GPP and demand further investigation. An increase in GPP due to
clouds can reach 32 %–33 % for mixed forests and 21 %–26 %
for coniferous forests with an open canopy. Other relevant questions include
cloud effects on the radiation regime and ecosystems' GPP on annual scale and
the investigation of potential aerosol effects on the evolution of clouds over
forests.