In vegetation canopies with complex architectures,
diffuse solar radiation can enhance carbon assimilation through
photosynthesis because isotropic light is able to reach deeper layers of the
canopy. Although this effect has been studied in the past decade, the
mechanisms and impacts of this enhancement over South America remain poorly
understood. Over the Amazon deforestation arch large amounts of aerosols are
released into the atmosphere due to biomass burning, which provides an ideal
scenario for further investigation of this phenomenon in the presence of
canopies with complex architecture. In this paper, the relation of aerosol
optical depth and surface fluxes of mass and energy are evaluated over three
study sites with artificial neural networks and radiative transfer modeling.
Results indicate a significant effect of the aerosol on the flux of carbon
dioxide between the vegetation and the atmosphere, as well as on energy
exchange, including that surface fluxes are sensitive to second-order
radiative impacts of aerosols on temperature, humidity, and friction
velocity. CO2 exchanges increased in the presence of aerosol in up to
55 % in sites with complex canopy architecture. A decrease of
approximately 12 % was observed for a site with shorter vegetation. Energy
fluxes were negatively impacted by aerosols over all study sites.
The composition of the atmosphere is determined by the emission and
transport of gases and aerosols at different scales, chemical and
microphysical processes, wet and dry deposition, or by the distribution of
the land surface and oceanic ecosystems around the globe. These processes
are represented by the biogeochemical cycle, and they involve interactions
between different components of the Earth system. These interactions are
generally nonlinear and might produce negative or positive radiative
forcing signals in the climate system (Forster et al., 2007).
The state of the climate system is governed by the energy balance, which is
defined as the amount of energy going into the climate system minus the
amount of energy dissipated by it mostly through the emission of longwave
radiation. In the long term, the amount of solar radiation absorbed by
the Earth's atmosphere and surface is balanced out by the same amount of
longwave radiation emitted by them. About half of the solar radiation is
absorbed by the Earth's surface. This energy is transferred to the
atmosphere by heating the air in contact with the surface, through
evapotranspiration and longwave radiation emission, which can be absorbed by
clouds and greenhouse gases. The atmosphere in turn emits longwave energy
back to the surface as well as to space (Kiehl and Trenberth, 1997; Trenberth, 2009).
The Earth's climate system has undergone a number of changing processes
considered “natural” throughout the history of the planet. The average
temperature of the planet has always been directly linked to the amount of
CO2 in the atmosphere. The uncertainties on the energy balance
associated with greenhouse gases have been reduced over the past years;
however, uncertainties associated with atmospheric aerosols remain
substantial, despite the various advances achieved since the first studies
relating aerosols and the climate system (Boucher et al., 2013). Aerosols
are technically defined as solid or liquid particles suspended in the air
(Seinfeld and Pandis, 2006), which can be emitted from natural or
anthropogenic sources. They can be found in different shapes and chemical
composition, which may vary according to their source of emission and/or
processes they are subjected to once in the atmosphere. They can also be
classified into primary, i.e., when they are directly emitted, or secondary,
i.e., when they are formed in the atmosphere through a physicochemical
process known as gas–particle conversion.
The typical diameter of aerosols ranges from a few nanometers to tens of
micrometers (Seinfeld and Pandis, 2006), and it can impact their time spent
in the atmosphere and their ability to interact with radiation.
Aerosols can travel long distances which increase the time they are exposed
to radiation and the probability of interaction with clouds. In addition,
the accumulation mode (0.1 µm < particle size < 2.5 µm) interacts most effectively with solar radiation, although the
nuclei-mode aerosols (0.1 µm > particle size) are mostly
found over the continents and coastal areas, directly related to human
activities. Nuclei-mode aerosols account for about 10 % of the aerosols of
the globe (Textor et al., 2006). Aerosols emitted by natural sources are the
dominant type in the climate system. In climate models, aerosols are
generally divided into five general categories: dust, marine spray, sulfur
aerosols or sulfate, and carbon compounds, which are divided into two
subcategories: particulate organic material and black carbon (Chin et al.,
2002).
The direct radiative effect of aerosols can absorb and scatter radiation in
the shortwave band and longwave band, which directly impacts the radiative
balance of the climate system (Forster et al., 2007). The net impact of the
direct radiative effect of aerosols decreases the available solar energy at
the surface. In the last decades, there has been a 2.7 % reduction
observed in the incident direct solar irradiance at the surface, a
phenomenon known as “global dimming” (Stanhill and Cohen, 2001). Although
the mechanisms associated with the direct radiative effect of aerosols are
well described in the literature, uncertainties are still significant,
especially with respect to the spatial and temporal distribution of the
aerosols in the atmosphere (Forster et al., 2007; Fletcher et al., 2018). In
addition, the radiative effects of aerosols can impact different
compartments of the climate system, such as the biosphere, for example.
The biosphere interacts with the climate system through biogeophysical and
biogeochemical processes, which can cause both positive and negative
responses on the radiative balance, and some of these responses, especially
at a local scale, can be quite significant (Wang et al., 2014; He et al.,
2017; Persad and Caldeira, 2018).
The overall reduction in incident radiation at the surface has negatively
impacted gross primary productivity (GPP) in light-limited ecosystems
(Black et al., 2006; Unger et al., 2017; Ezhova et al., 2018). In addition,
a decrease in total solar radiation is followed by an increase in its
diffuse component, which in turn can increase plant productivity due to a
higher production efficiency per unit of incident radiation in the canopy,
an effect known as “the diffuse fertilization effect” (Kanniah et al.,
2012). In ecosystems with densely vegetated canopies, the direct shortwave
radiation reaches the upper part of the vegetation canopy, while most leaves
at medium canopy height or at bottom canopy layers remain shaded. Carbon
assimilation increases under atmospheric conditions with a moderate aerosol
load and high fraction of diffuse radiation (Niyogi et al., 2004) and
decreases as the concentration of aerosols increases to a level where the
overall radiation is substantially reduced (Yamasoe et al., 2006).
The spatial distribution and nature of the shortwave radiation is critical
for the productivity of terrestrial ecosystems and further studies on the
impacts of light limitation on vegetation productivity are needed. In light-limited ecosystems such as rainforests and temperate forests, the radiative
impact of aerosols on vegetation productivity must be evaluated in
combination with other environmental factors, such as temperature and water
(O'Sullivan et al., 2016). With fieldwork and numerical studies in the
Australian savannah, Whitley et al. (2011) suggested that vegetation
productivity was more influenced by light limitation than by water
limitation. Actually, the response of photosynthetic capacity to an increase
in the concentration of atmospheric aerosol has been extensively studied
over almost the past 2 decades (Roderick et al., 2001; Gu et al., 2002;
Niyogi et al., 2004; Yamasoe et al., 2006; Oliveira et al., 2007; Mercado et
al., 2009; Doughty et al., 2010; Kanniah et al., 2010, 2012, 2013; Zhang et al., 2010; Li et al., 2014; Rap et al., 2015; Tang and Dubayah,
2017) through observations (Steiner et al., 2013) and modeling (Moreira et
al., 2017) frameworks.
In this paper, however, we investigate the impact of atmospheric aerosols on
the carbon balance over three sites in South America in the deforestation
arch of the Amazon forest, making use of a novel approach and combining two
different types of modeling techniques. First, we make use of artificial neural networks (ANNs), a machine learning algorithm that can approximate
any nonlinear deterministic function (Gentine et al., 2018), to build a new
modeling framework from scratch relating a number of state variables
(radiation, temperature, humidity, as well as the aerosol optical depth)
to energy and mass fluxes for three study sites representing a range of
structurally different ecosystems in the Amazon; and second, we use a radiative
transfer scheme driven with a unique aerosol optical model developed in
do Rosário et al. (2009) and described in do Rosário et al. (2011). A
recent study has performed a modeling evaluation of the radiative impact of
aerosols over a temperate forest with complex structure in the USA (Lee et
al., 2018), but the studies in the Amazon remain mostly observational.
Machine learning has significantly advanced in the last decades, being
applied to a number of different research areas and more recently used in
Earth system studies as a powerful tool to link physical process models with
data-driven relationships (Schneider et al., 2017; Huntingford et al., 2019;
Reichstein et al., 2019; Watson-Parris et al., 2019). There are a number of
different machine learning techniques that have been applied to Earth system
studies including random forests (Rodriguez-Galiano et al., 2012; Yang et
al., 2016; McGovern et al., 2017), model tree ensembles (Jung et al., 2010;
Yang et al., 2016), empirical orthogonal functions (Yang et al., 2019),
principal components analysis (Gosh and Mujumdar, 2008), and ANNs (Krasnopolsky
et al., 2005; Goyal et al., 2014; Gentine et al., 2018; Nguyen et al., 2018;
Buckland et al., 2019; Wu et al., 2019).
We have chosen ANNs over other machine learning methods because once
trained, they are computationally efficient, as most of the computational
demand is required during the training phase. Perhaps this is one of the
main reasons for their large applicability in several different studies, an
advantage over more resource-demanding machine learning techniques. Although
ANNs are highly data dependent, sometimes leading to problems of
over-fitting and generalizations, ANNs are a good approach for problems with
large datasets because they allow any number of input variables.
The main goal of this paper is to investigate, step by step, the effects of
the atmospheric aerosol on the variability of CO2 and sensible (H) and
latent (LE) heat fluxes in the Amazon region over the deforestation arch,
based on the study of three specific experimental sites. The use of ANNs
allows us to build up a statistical modeling framework relating meteorological
(i.e., temperature, humidity, friction velocity) and radiative variables
(i.e., incident photosynthetically active radiation (PAR) at the top of the
canopy, the percentage of diffuse PAR) to mass and energy fluxes in the
forest, in a first approach to address the radiative impacts on mass and
energy fluxes. The follow-up analysis is based on the derivation of a second
group of ANNs relating radiative variables to meteorological ones in order
to identify the second-order effect of aerosols on surface fluxes. Finally,
we make use of the radiative transfer (RT) theory with a process-based RT
model to create a hypothetical scenario without aerosols and estimate the
potential fluxes of mass and energy, not only considering the first-order
impact of aerosols, but also the second-order impact by making use of two
distinct groups of ANNs.
With this approach, this study evaluates (i) the impacts of aerosol optical
depth at 550 nm on fluxes of heat, water, and CO2; (ii) the
characteristics of aerosol present in each of the experimental sites; and
(iii) the isolated impact of aerosols on mass and energy fluxes through
statistical modeling with ANNs, in comparison to other environmental
factors, such as temperature, moisture, and turbulence.
Geographical representation of the northern region of Brazil. The
darker green area represents remaining forest. The black dots indicate the
location of the study sites: Rebio Jaru, RO (west), Sinop, MT (center), and
Bananal Island, TO (east). Boxes show the temporal distribution of the
aerosol optical depth at 550 nm over the three sites derived from MODIS from
2003 and 2008 and the monthly mean values for the period, with the standard
deviation represented by vertical bars.
Materials and methodsSites
The deforestation arch consists of 248 municipalities in Brazil going from
the state of Rondônia to the state of Maranhão, across the Brazilian
savannah and Amazonian biomes (Araújo Junior et al., 1998), and it is about 3000 km long and up to 600 km wide. Between July and October 1987, the region of the deforestation arch lost approximately 50 000 km of
forests in the states of Pará, Mato Grosso, Rondônia, and Acre and
released more than 500×106 t of carbon into the atmosphere, 44×106 t of which were in the form of CO2 (Song et al., 2015). The three flux
sites evaluated in this paper are in the Brazilian deforestation arch
(Fig. 1).
Bananal Island
Bananal Island is the largest river island on the planet, with
approximately 2×106 ha and measuring almost 360 km in the north–south
direction and 80 km in the east–west. Bananal Island is a floodplain
region. The flux tower of Bananal Island was installed in the northern part
of the island, within the boundaries of Canton State Park, by the Laboratory
of Climate and Biosphere of the Institute of Astronomy, Geophysics, and
Atmospheric Sciences of the University of São Paulo. The average height of
the canopy is approximately between 15 to 18 m. The instrumental platform
was mounted in a 40 m high structure located approximately 2 km east of the
Araguaia River, at the geographic coordinates 9∘49.27′ S,
50∘08.92′ W, at 120 m a.s.l. The data monitoring started on the
27 October 2003 and the project was based on the facilities of the
Canguçú Research Centre of the Instituto Ecológica – UFT,
approximately 20 km south of the micrometeorological tower (Borma et al.,
2009).
Rebio Jaru
In the northeastern part of the state of Rondônia, Brazil, the Jaru
Biological Reserve has approximately 60 000 ha. The entire basin of the
Tarumã river is in the reserve and covers about 75 % of the unit. The
land has a large number of small streams and springs keeping a moist
vegetation throughout the year. The vegetation is mainly characterized as an
open tropical forest with some palm trees. There are also a few areas with
lianas and bamboo trees, as well as small spots of dense ombrophile forest,
characteristic of the Amazon region (IBAMA, 2006). The average height of the
canopy is approximately 35 m, but some trees can reach up to 45 m. The tower
is located 1240 m from the river bank in a more preserved region (Gomes,
2011). Data from Rebio Jaru were collected from August to October 2007,
at the geographic coordinates 10∘04.71′ S, 61∘56.01′ W, at 148 m a.s.l. Eddy covariance instruments were installed at 62 m in height
(Bosveld and Beljaars, 2001).
Sinop
This study site is located in an area of logging with forest management in
Fazenda Maracaí. The farm is located approximately 50 km to the northwest of the center of Sinop, Mato Grosso, Brazil, at coordinates
11∘24.70′ S, 55∘19.39′ W, at 423 m a.s.l. The
Maracaí farm consists of a transitional tropical forest (between
Brazilian savannah and open ombrophile forest) with a continuous canopy,
composed of trees 25 to 28 m in height (Vourlitis et al., 2008). The
vegetation consists of perennial trees that are characteristic of the
transition forest in Mato Grosso. The diversity is high, and there is no
predominance of a single tree species. There are approximately 80 species
and 35 families of trees with diameter at breast height around 10 cm. The
climatological seasonality for the ecotone of the transition forest is
similar to that of the tropical and closed forest. The sonic anemometer was
installed at 42 m height. The site is part of the LBA (Large Scale
Biosphere-Atmosphere Experiment in Amazonia) integrated project and has been
carrying out measurements of this nature since 1999.
Flux tower data
Surface fluxes of CO2, sensible heat, and latent heat were obtained
through the eddy covariance technique applied to data collected with sonic
anemometers and infrared gas analyzers installed in flux towers located at
Bananal Island, Sinop, and the Jaru Biological Reserve (Rebio Jaru). Fluxes
were obtained with a temporal resolution of 30 min. Measurements of
meteorological variables, such as temperature, relative humidity, and wind
speed were collected. Except for the tower in Sinop, the other towers
presented radiative measurements such as incident solar radiation and
incident and reflected PAR. For Sinop, these variables were calculated by a
radiative transfer model, libRadtran, further described in Sect. 3.1.
Geographic coordinates, altitudes, flux tower height, and the
sensors used in the three experimental sites.
1 Values obtained from the radiative transfer model.
2 Multifilter Rotating Shadowband Radiometer.
Geographic coordinates, altitudes, flux tower heights, and the type of
sensors used in each one of the three sites are presented in Table 1. A Multifilter Rotating Shadowband Radiometer data provided aerosol
optical depth at 550 nm estimates per minute, and 30 min averaged values
were calculated, available only for the Rebio Jaru site.
The study period of this work was limited to the dry season for the following reasons: (a) the maximum numbers of fire outbreaks occur in August,
September, and October; (b) in terms of the temporal distribution
of the aerosol optical depth, there is a significant increase in the months
of August, September, and October; (c) the analysis of the
intrinsic properties of the aerosols becomes important because it indicates the chemical composition and size of the particles. Only the
months of August, September, and October were considered in this analysis in
order to keep consistency of the type of aerosols being evaluated.
MODIS data
The aerosol optical depth (AOD) at 550 nm used in this study was obtained
from the Moderate Resolution Imaging Spectroradiometer (MODIS) for Bananal
Island and Sinop. In Rebio Jaru AOD values were obtained in
situ with a shadow band radiometer (Multifilter Rotating Shadowband Radiometer, MFRSR) (Table 1). MODIS is an
instrument on board the Terra and Aqua satellites. Terra's orbit
around the globe is synchronized and runs from north to south across the
Equator line in the morning (∼10:30 local time), while Aqua presents passages from south to north by the Equator in the afternoon
(∼13:30 local time). MODIS observes the entire ocean surface
of the Earth and almost the entire terrestrial surface in a period of 1 to 2 d and acquires data in 36 spectral bands. The aerosol product from MODIS
is the aerosol optical depth measured over the oceans and a portion of the
continents, globally. In addition, the aerosol size distribution is derived
over the oceans, and the aerosol type over continents. Daily MODIS level-2
data are produced with a spatial resolution of 10×10 km2 over nadir.
The level 2 product was chosen due to its estimation of the aerosol
retrieval uncertainty to assist with error analyses as well as a best
estimate of the aerosol optical depth data with quality assurance flags
already applied. Although spatial resolution is coarser at level 2, lower
product uncertainty improved our analysis.
Two groups of MODIS aerosol data products were collected:
MOD04_L2, containing data from the Terra
platform, and MYD04_L2, containing data from
the Aqua platform. The MODIS aerosol product is used to build the
climatological variance of aerosols, sources and sinks of specific types of
aerosols (e.g., sulfate aerosols, biomass burning), the interaction of
aerosols with clouds, and atmospheric corrections of surface reflectance
obtained from remote sensing.
Pre-assumptions about the general structure of aerosol size distribution are
necessary for the inversion of the MODIS data and the distribution is
described as two log-normal curves: a unique mode, to characterize the
particles of accumulation mode (aerosol radius < 0.5 µm) and a
single coarse mode to describe dust and salt particles (aerosol radius
> 1.0 µm). The aerosol parameters that are thus recovered
are the relationship between the two modes (fine and coarse), spectral
optical depth, and mean particle size. The quality control of these products
is based on the comparison with terrestrial stations and climatology. We
used instantaneous and monthly fields of the aerosol optical depth at 550 nm
(AOD550nm), referring to collection 051 (Hubanks, 2012), level 2. The pixels
over the flux towers were considered. Episodes contaminated by clouds were
excluded from the analysis.
The MODIS aerosol data were acquired at the moment of each satellite overpass
(Terra and Aqua) over the site of interest, and a single point was used to
represent the 30 min interval. Uncertainties associated with each
variable were estimated from the standard deviations obtained for the
30 min intervals, except for the MODIS AOD550nm measurements, in which
the uncertainty of the AOD550nm in an area of 10×10 km2 was
estimated following Levy et al. (2009) as
στ=±(0.05+0.15τ).
In Rebio Jaru, in situ AOD measurement was conducted in a relatively limited
period of time, covering only part of the dry season of 2007, yet the high
temporal resolution of the aerosol optical depth data (every minute) enabled
a more robust analysis of the diurnal cycle. On the other hand, at Bananal
Island and in Sinop, the time series of flux measurement was longer (from
2003 to 2008 and from 2005 to 2008, respectively). However, AOD data were
limited to a maximum of two points a day from MODIS, but the analysis of these
experimental sites presented possibilities of characterization of the
interannual cycles.
Models
In this section, we discuss the two models used in this study: a physical
process-based atmospheric radiative transfer model – libRadtran (Mayer and
Kylling, 2005) – and a statistically based model – the artificial neural
networks. First, we describe the radiative transfer model; second, we
present the machine learning technique (ANNs), and describe the specific
methodology used in our experiments. Finally, we describe statistical
metrics used to evaluate our results.
Radiative transfer model
The radiative transfer model libRadtran1.6-beta (Mayer and Kylling, 2005)
was used to perform calculations with the incident solar radiation at the
top of the atmosphere (TOA) specified for the Amazon region with a tropical
atmosphere profile. Ozone concentrations were set to 300 DU based on values
obtained by do Rosário et al. (2009), who found an average value of
integrated ozone content in the atmospheric column of 257 DU. In this paper,
surface albedos from the International Geosphere-Biosphere Programme (IGBP)
classification (Loveland and Belward, 1997) were used.
For the three sites, the surface type was set to a deciduous broadleaf
forest. The day of the year provides information about the Earth–Sun
distance, which affects the amount of radiation incident at the TOA, and it
was also used as input to the code. The correction for molecular absorption,
parameterized from the LOWTRAN model (Pierluissi and Peng, 1985) and
adopted by the SBDART code (Ricchiazzi et al., 1998), was implemented. The
resolution of the corrections for molecular absorption was 0.5 nm at
wavelengths below 350 and 1.0 nm above. The DISORT method (DIScrete
ORdinate Radiative Transfer Solvers) was used to solve the radiative
transfer equations because of its versatility and broad use (Mayer et al.,
2010). Four streams were used in the simulations performed in this paper.
Previous studies have shown that differences in incident irradiance at the
surface with at least four streams are minimal (Ma et al., 2011). The PAR
waveband ranges between 400 and 700 nm and total solar radiation between 280
and 4000 nm.
The input data required to describe the intensive optical properties of the
aerosols were obtained from models created with the Mie theory from
do Rosário (2011), derived from the Aerosol Robotic Network (AERONET)
products for three experimental sites: Alta Floresta, Abracos Hill, and Rio
Branco. These models carry spectral information about the linear extinction
coefficient (Q) in km-1, the single scattering albedo (ω0), and the asymmetry factor for the phase function, calculated from
the Henyey–Greenstein method (Henyey and Greenstein, 1941). The spectral
resolution of the aerosol model in the PAR spectral region was 25 nm, and
over the entire solar spectrum it varied from approximately 10 nm at shorter
wavelengths up to 700 nm at longer wavelengths.
Three optical models were developed for the biomass burning aerosol emitted
over Amazon, which are different in scattering and absorption properties: a
more scattering, an intermediate type, and a more absorbing aerosol type.
Also, in this study, sensitivity analysis performed with data from the
Biological Reserve of Jaru showed that the more scattering aerosol model is
the one that best represented the biomass burning aerosols of that season,
associated with higher values of AOD550nm. Therefore, in this study, the
aerosol model used for all three study sites was the more scattering type.
The specific input parameters of the radiative transfer equations are solar
zenith angle and the aerosol optical depth at 550 nm. The model provides the
integrated value of the downward direct irradiance, the downward diffuse
irradiance, and the upward diffuse irradiance, at the altitude determined by
the user. All calculations were determined at the surface. From these
values, it is possible to obtain the descending global irradiance (the sum
of the contributions of the direct and diffuse irradiance incident at the
surface) and the diffuse fraction, i.e., the ratio between the diffuse and
incident global irradiance at the surface.
Artificial neural networks
ANNs have great potential because of their
capacity to represent the complexity of the phenomena that are analyzed
through them. Traditionally, physical simulation systems have great
difficulty in reproducing complex responses of natural events because
unknown processes are often unresolved or represented through simplified
approaches called “parameterization schemes”, which can limit forecast
predictive skill (Huntingford et al., 2019). ANNs can be considered a
network of many relatively simple processors, called units or nodes, in
which there is a small amount of local memory. In ANNs, these basic local
memory units are commonly layered and communicate through channels or
connections and operate only on their own media, that is, with their local
data and their input parameters (Papale and Valentini, 2003). They can be
seen as robust parallel processors with a natural ability to store knowledge
gained from experimentation and make this learning available for future use.
This means that, with this method, it is possible to create hypothetical
scenarios to evaluate the behavior of the output variables, in relation to
possible modifications in the input variables.
ANNs are trained from examples contained in the total set of input data. The
“weights” are randomly provided at the beginning of the process and
modified according to the rule used in the backpropagation method. In this
paper, two methods of resolution of ANNs were used. (i) The multilayer
perceptron (MLP) is a direct and unidirectional power supply model of
artificial neural networks, which maps the input datasets to an appropriate
set of output data. The MLP uses the backpropagation technique to train the
networks. The most commonly used functions in this method are both sigmoid
(Bayram et al., 2016). (ii) The radial basis function (RBF) uses
functions of radial basis as a function of activation of the nodes of the
neural networks. The RBF method typically has three layers: the input, the
intermediate, with a nonlinear activation function, and the output, with a
linear function. The input parameters are modeled as a vector composed of
real numbers, and the outputs are scalar functions with vector input values
(Bayram et al., 2016).
The MLP is a function f: RD→RL, where D is the size of the
input vector x and L is the size of the output vector f(x), such that, in matrix
notation,
fx=G(b2+W2(s(b1+W(1)x))),
with bias vectors b(1), b(2); weight matrices W(1), W(2),
and activation functions G and s. W(1) is the weight matrix connecting the
input vector to the hidden layer. Choices of s include tanh and the logistic
sigmoid function with sigmoid(a)=1/(1+e-a).
The RBF is a function with respect to the origin or a certain point c:
fx=f(||x-c||).
RBF neural networks are good for novelty detection because each neuron is
centered on a training example, and so inputs far away from all neurons
constitute novel patterns; but it is not as good at extrapolation because it
gives the same weight to every attribute as they are considered equally in
the distance computation.
For all ANNs used in this paper, 200 neural networks were generated for each
one of the different output variables, from all three study sites, and only
the one with the smallest error between modeled and observed output was
evaluated. From the total dataset, 70 % of the data was randomly chosen
and used in the training process of the ANNs, 15 % in the validation
process, and 15 % in the testing process. The set used in the training
process has the function of exemplifying relations between inputs and
outputs to the ANN. This data partitioning was selected to allow the vast
majority (> 2/3) of the data to be used for training, and the
remaining data (less than one-third) were equally partitioned between validation
and testing because both processes are thought to have the same importance
in the ANN construction.
We have used the two most recommended back-propagation algorithms for
optimization of ANNs (Rojek, 2017) – the Broyden–Fletcher–Goldfarb–Shanno
(BFGS) and radial basis function training algorithm (RBFT) – and the sum of
squares (SOSs) to choose between MPL and RBF by selecting the one which
presented the smallest deviation to the training data.
The variables used to train the ANNs were incident photosynthetically
active radiation at the top of the canopy (PARi) in watts per square meter,
the partitioning of photosynthetically active radiation in its diffuse form
(PARdif) in percentage, derived from the method of Reindl et al. (1990), the
solar zenith angle (SZA) in degrees, the average air temperature in the
canopy (T) in degrees Celsius, the vapor pressure deficit (VPD) in kilopascal, the
friction velocity (u*) in meters per second, and the aerosol optical depth
at 550 nm (AOD550nm). Therefore, seven variables were used as inputs of the
ANN, and the flux of carbon dioxide (FCO2), in micromol of CO2 per square meter per second, was selected as output.
The best carbon flux ANN for all sites presented similarities such as all
three being generated from the RBF training model, using a Gaussian function
for the activation of the hidden units, and the identity function for the
activation of the output. However, the ANNs presented a different number of
hidden units. The network generated for Bananal Island presented 30
hidden units, the one generated for Sinop used 21, and that for Jaru 19.
Basically, the larger the number of hidden units in a neural network, the
more robust the model is; i.e., the neural network has a greater capacity to
model more complex relationships. At the end, the number of hidden units
provides an indication that the database of the three analyzed sites
presented different complexities in the relationship between the variables
introduced at the moment of the construction of the networks. However, the
functions that best managed to describe these relations were the same and
the difference between them is the error magnitude between output data used
for training and the model output.
The performance of the ANN results was evaluated using the coefficient of
determination (r2), which is the Pearson coefficient (r) squared, root mean square error (RMSE), and mean absolute error (MAE), defined as
r2=∑p(y-mean(y))(t-mean(t))∑p(y-mean(y))2(t-mean(t))22,
where p is the total number of data points used for validation only,
excluding training and testing, y is the output values of the generated
ANNs, and t is the values derived from the flux tower data (FCO2, H,
and LE) or meteorological variables (T, VPD, and u*).
RMSE is used to evaluate differences between the
values predicted by a mathematical or physical model (y) and the observed
values (t). RMSE is a good measure of the accuracy of a model, but only to
compare calculations and observations of the same variable, since it is
scale dependent. It is obtained by the following equation:
RMSE=∑p(y-t)2p.
Finally, the use of the MAE does not take into account
whether an error was overestimated or underestimated once it is characterized as
the average of the errors committed by a model and can be obtained from the
following equation:
MAE=1p∑py-t.
ResultsEvaluation of ANNs for <inline-formula><mml:math id="M102" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">CO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>, sensible heat, and latent heat fluxes
In the absence of clouds, aerosols were responsible for changing the amount
of solar radiation available at the surface and the partition between direct
and diffuse radiation. This, in turn, impacted surface temperature, and water
vapor, among other changes able to affect photosynthesis.
Flux data were combined with meteorological and satellite data. It is
important to emphasize that the flux data have a sample interval of 30 min, while the satellite data are a “snapshot” of the atmosphere when the
passage occurs in a very short time interval, which registers a value
referred to in a single minute but over a 10×10 km2 area.
From the integration of the database it was possible to make an analysis
using ANNs. Some precautions were taken in order to avoid a misleading
analysis of the ANNs. For example, values of u* smaller or equal
to 0.2 m s-1 were excluded from the training, testing, and validation
processes to avoid uncertain CO2 flux data collected in episodes with
less turbulence and therefore a less mixed atmosphere. All data points for
all variables above or below three standard deviations were excluded from
the input datasets of the ANNs because they were considered to be outliers.
Seven variables were used as inputs of the ANNs: photosynthetically active
radiation incident on surface (PARi), in watts per square meter; the partition of
photosynthetically active radiation in diffuse form (PARdif), in percent,
derived from the method of Reindl (1990); the solar zenith angle (SZA), in
degrees; the mean air temperature in the canopy (T) in degrees Celsius; the
vapor pressure deficit (VPD) in kilopascal; the friction velocity (u*), in
meters per second; and the aerosol optical depth at 550 nm (AOD550nm). The flux of
carbon dioxide (FCO2) (in µmol CO2 m-2 s-1) was
selected as output.
FCO2 (µmol CO2 m-2 s-1), H
(W m-2), and LE (W m-2) calculated from flux
tower data through eddy covariance (x axis) versus modeled by ANNs
(y axis), with r2, RMSE, and MAE. The 1:1 straight line (thick) and the
linear fit (thin) are also represented for the three study sites.
Figure 2 shows the FCO2 dataset used for validation, obtained
through the eddy covariance method on the x axis (eddy) and the ones
calculated by the generated ANN, on the y axis, with the respective
determination coefficient (r2), RMSE, and MAE, for all three sites.
The Rebio Jaru site presented the highest r2 (0.67) of the three
evaluated sites, which represents a high correlation between the measured
and modeled CO2 flux data. This site presented continuous AOD550nm
data coverage throughout the day. At the other two sites, a single day had a
maximum of two AOD550nm data points associated with the passages of the
Terra and Aqua satellites. This result indicates that a continuous daytime
coverage of AOD generated a more robust ANN influencing FCO2 with more
predictability.
Box and whisker plots with extreme data points added for the
diurnal cycle of FCO2 (µmol CO2 m-2 s-1), H
(W m-2), and LE (W m-2) calculated from flux
tower data through eddy covariance (EC) (gray) and modeled by ANNs (red) for 6 d (23, 24, 25 and 29 August and 1 and 10 September) over the dry season of 2007 in Rebio
Jaru. The height of the box portion is given by the interquartile range of
the dataset and extends from the 25th to 75th percentile. The
horizontal bar within the box denotes the median value. The ends of the
whiskers are drawn to the10th and 90th percentile values. The
extreme values are points at the maximum and minimum.
Figure 3 shows box and whisker plots for the diurnal cycle of
FCO2 (µmol CO2 m-2 s-1), H (W m-2),
and LE (W m-2) for flux tower data obtained through the eddy
covariance (EC) method and modeled with ANNs for 6 d (23, 24, 25 and 29 August and 1 and
10 September) over the dry season of 2007 in Rebio Jaru. It is possible to
see that on all evaluated days, the ANN results represent the
measured data well. Although for the three fluxes (FCO2, H, and LE), the
ANNs are not able to capture the extreme values of the distribution (the
10th and 90th percentiles), the median and both quartiles are well
represented. During the evaluated period (August, September, and October 2007), the mean value of FCO2 was -12.2µmol CO2 m-2 s-1, while the average FCO2 generated by the ANN for the
same period was -12.4µmol CO2 m-2 s-1, a relative
difference of 1.9 %.
The Bananal Island site presented the lowest values of RMSE (3.20) and MAE
(2.43). because of its long temporal series, from October 2003 to December
2008, with 320 input points, which was important to establish a more robust
mapping of the intra-seasonal variability. Throughout the evaluated period,
the mean observed FCO2 for Bananal Island was -7.0µmol CO2 m-2 s-1, while the average FCO2 generated by the ANN was -7.1µmol CO2 m-2 s-1, a relative difference of 1.3 %.
The Sinop site presented the largest discrepancies between the model and
observed FCO2 with an r2 of 0.37, RMSE of 5.69 µmol CO2 m-2 s-1, and MAE of 4.78 µmol CO2 m-2 s-1. Because its
database is of intermediate size compared to the other sites (237 points) and no
PAR measurements were performed at Sinop, values were generated with the
radiative transfer model libRadtran. The errors associated with the outputs
of the radiative transfer model were included in the neural network training
process. The relative difference between the mean observed and simulated
FCO2 was 1.2 %.
In order to evaluate the radiative effects of aerosol optical depth on
turbulent energy fluxes, the same type of analysis as performed for carbon
dioxide flux was done for sensible heat flux (H) and latent heat flux (LE).
The evaluated period was also the same: August, September, and October, the
dry and burning season in the region. For all three experimental sites the
ANNs were made from the same variables used in the networks related to
FCO2.
All data points above or below three standard deviations were excluded from
the input sets because they were considered to be outliers and because of the choice
of ANNs.
As for FCO2, Rebio Jaru presented the highest r2 (0.87) for
sensible heat flux results. However, the same behavior was not observed when
evaluating latent heat flux, which presented a relatively lower r2
(0.58) in comparison. In general, the overall results of LE compared to H
presented relatively smaller r2 values. The diurnal cycle of H is more
well-behaved in relation to LE as it depends on the diurnal cycle of
temperature. Latent heat flux, on the other hand, is greatly influenced by the advection of moisture coming from other sources not exclusively related to
stomatal opening by the local vegetation. The presence of springs nearby
could explain peaks in local maxima of latent heat flux. This can be seen in
Fig. S3 in the Supplement, which shows the diurnal cycle (for
some days of the experiment) of measured H and LE fluxes (black dots) and
the respective curves calculated from ANNs (red lines).
During the evaluated period (August, September, and October 2007), the mean
H observed was 117.4 Wm-2, while the average of H generated by ANN for
the same period was 111.7 Wm-2, a relative difference of 4.9 %. For
LE, the mean value of the observed data was 218.1 Wm-2 and the
average value generated by ANN was 218.7 Wm-2, a relative difference
of 0.3 %. Although the analyses performed between the validation data and
those obtained through ANNs presented better results for H, the relative
difference between the mean values of LE – calculated and observed – was
lower.
It is important to emphasize that the analysis performed between the
validation data and the modeled data generated by ANNs is a way of
determining if the neural network is robust. This does not necessarily mean
that a low r2 indicates that a given ANN has low predictability. The
evaluation of the relative deviation is a way of measuring the accuracy of
an ANN. Although it is possible to note in Fig. 2b that the ANN
of H could better approximate the measured values, the relative error
associated with ANN of LE was around 4.6 % lower.
Bananal Island presented intermediate r2 values (0.42 for H and 0.35
for LE), which indicates an average correlation between the observed and the
modeled data. For relative deviations, the values were lower than 3 %
for H data and lower than 1 % for LE.
For Sinop, the analyses of H presented an r2 of 0.53, considered from
medium to high performance of the ANN, and a relative deviation of 0.5 %.
For LE, the r2 was 0.27 and the relative error was 1.2 %.
Modeling <inline-formula><mml:math id="M226" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">CO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> fluxes with ANNs with constant temperature and
moisture
From the generated ANNs, tests were performed to determine the behavior of
FCO2 in different scenarios for all three evaluated sites. In the first
test, the values of T, VPD, and u* were fixed in order to isolate aerosol
radiative effect on the availability of photosynthetically active radiation
at the surface and the partitioning of it in its direct and diffuse forms.
This first test is hypothetical, since it is known that the fixed variables
also suffer influences of AOD550nm. In the test, AOD550nm varied from 0.0 to
2.0 and the radiative transfer model libRadtran was used to estimate values
of PARi and PARdif values for three different solar zenith
angles (15, 30, and 45∘). The results related to Bananal Island can be seen in Fig. 4.
The other sites showed similar behavior.
PARi (Wm-2) (continuous lines) and PARdif ( %) (dashed lines)
versus AOD550nm, for three different SZAs (15,
30, and 45∘) calculated by the radiative
transfer model, libRadtran, for Bananal Island for a characteristic day of
the dry season.
The values of T, VPD, and u* were fixed at the “optimal point” of
FCO2, i.e., for which values of T, VPD, and u*FCO2 presented
the largest absolute values. For the other sites, arbitrary values were
fixed close to those found at Bananal Island. Table 2 shows
the values of T, VPD, and u* fixed for each one of the evaluated sites.
Figure 5 shows the variation in FCO2 in relation to AOD550nm
for three different SZAs (15, 30, and
45∘) at the three evaluated sites. For Bananal Island and
Rebio Jaru, the increase in AOD550nm generates an initial decrease in the
absolute values of FCO2 for almost all SZAs, except for the solar
zenith angle of 30∘ at Bananal Island, which appears to have
no impact on CO2 flux until AOD550nm ∼0.25. However,
from this value on, the FCO2 values show a decreasing trend.
Modeled FCO2 (µmol CO2 m-2 s-1), H
(W m-2), and LE (W m-2) via ANNs versus
AOD550nm for three SZAs (15, 30,
and 45∘) for three study sites. Values of temperature,
VPD, and u* were kept constant. PARi and PARdif were obtained
through libRadtran.
Fixed values of temperature (T), vapor pressure deficit (VPD),
and friction velocity (u*) for three sites evaluated in the first test.
Bananal Island –Rebio Jaru –Sinop –TOROMTT (∘C)28.529.029.0VPD (kPa)1.52.02.0u* (m s-1)0.30.20.2
The FCO2 at Bananal Island presents a minimum point with the variation in AOD550nm, while in Rebio Jaru, FCO2 presents a more stable
behavior. At Bananal Island, after the minimum point of the curve,
FCO2 starts to increase with AOD550nm; i.e., there is an optimal range
of AOD550nm, which is dependent on the SZA. For SZA =15∘, this
interval is associated with AOD550nm between 0.59 and 0.93. For SZA =30∘, the interval is narrower, with AOD550nm ranging from 0.57 to
0.61. And for SZA =45∘, AOD550nm is between 0.45 and 0.55.
For higher values of AOD550nm (above 1.75), the behavior of FCO2 also
stabilizes but at different points, dependent on SZA.
In Rebio Jaru, on the other hand, this behavior associated with an “optimal
range” of AOD550nm influence on the FCO2 curve is not observed. It is
possible to identify a value of AOD550nm where the value of FCO2
stabilizes and does not present significant variations anymore. This value
of AOD550nm, associated with the stabilization of the curves, also appears
correlated with the solar zenith angle. For SZA =15∘, this
value of AOD550nm is 0.98, for SZA =30∘ it is 0.88, and for SZA =45∘ it is 0.65. When analyzing the results for Sinop, this behavior
of a decrease in the absolute value of FCO2 with the increase in AOD550nm
is not verified. The addition of AOD550nm values from 0.0 to 2.0 generates
increases in absolute values, that is, the CO2 changes between the
local vegetation and the atmosphere decrease with a higher aerosol load. This
behavior is observed up to a value of AOD550nm from where FCO2 value
stabilizes, depending on the SZA. For SZA =15∘, this value of
AOD550nm is 1.85, for SZA =30∘ it is 1.70, and for SZA =45∘ AOD550nm =1.25.
Modeling temperature, humidity, and wind with ANNs
In the first test, temperature, VPD, and u* were fixed as described in
Table 2, but these variables are directly related to the available
energy at the surface, and, therefore, these variables would also be impacted
in the presence of aerosols. Artificial neural networks were built from the
same database used to make the previous ANNs but varying its input and
output values.
The first variable to be evaluated was the temperature. Three variables were
used as input parameters in the training processes of the temperature ANN:
PARi (Wm-2), PARdif (%), and SZA (∘).
These variables were chosen as inputs for the neural network of temperature
because radiation directly impact surface temperature. The available energy
at the surface is proportional to PARi and PARdif, linked to the
diurnal cycle.
The second evaluated variable was VPD. This variable is related to
temperature, which is related to PARi, PARdif, and SZA. Thus,
these four variables were used as input values in the construction of the VPD
ANN.
Finally, u* is also affected by energy available at the surface but to a
lower degree once it is directly dependent on wind speed. All other
variables (except for the AOD550nm itself, in order to avoid overtraining)
were used as input values in the construction of the u* ANN.
Temperature (∘C), VPD (kPa), and u* (m s-1)
obtained from ANNs generated with observed values for the three study sites,
with respective r2, MAE, and RMSE. The 1:1 line (thick) and the best
linear fit (thin) are also shown.
Figure 6 shows data used for the validation, i.e., without
interference in the construction of the networks. The values of the x axis
correspond to the observed values and those of the y axis to the values
generated by the neural networks. The results for the neural networks of
VPD, for all evaluated sites, were the most compatible with the values
observed, and they presented an r2 greater than 0.8. This is mainly
because VPD is closely related to temperature. The explanation of the
variance of VPD through the variables used as inputs of the ANNs was mainly
governed by temperature. At Bananal Island, temperature explained 50.5 %
of the VPD variance; in Rebio Jaru and Sinop, it explained 65.6 % and 38.1 %,
respectively. Temperature and friction velocity presented a moderate value
of r2 (between 0.2 and 0.5) for all cases. However, in the overall
sensitivity analysis, PARi was mostly responsible for the explanation
of the variance of temperature.
Isolating the radiative impact of aerosols on <inline-formula><mml:math id="M311" display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">CO</mml:mi><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula>, sensible heat,
and latent heat fluxes with ANN modeling
From the generated ANNs, a study was performed in order to identify what the
relative differences would be in FCO2, H, and LE fluxes between real
observed conditions and an atmosphere without aerosols, i.e., with AOD550nm =0.0 for the evaluated cases.
First, the radiative transfer model, libRadtran, was used to obtain
PARi and PARdif values for an atmosphere with AOD550nm =0.0.
For the calculations, the real values of SZA and day of the year were
used. The results for the Rebio Jaru site can be visualized in
Fig. 7. For the other study sites, the same behavior of the
curves is observed.
It is possible to identify that the observed values of PARi are
systematically lower than those obtained by the radiative transfer model,
while the opposite is observed for PARdif. Aerosol decreases the
incident shortwave global radiation at the surface by absorption and
scattering processes, while it increases the percentage of diffuse radiation
due to the scattering. From PARi and PARdif calculated for an
aerosol-free atmosphere, temperature, VPD, and u* were calculated through
the ANNs. The SZA values used as input in the libRadtran were the same as
those used in the ANNs.
All variables obtained by both models (libRadtran and ANNs) – except for the
values of AOD550nm, which were set to zero, and SZAs, which were kept the
same as in the original database – were used in calculations of fluxes of
CO2, H, and LE. Table 3 summarizes the results. The mean real
values and standard deviations of each of the variables of the initial set
of data, the mean model values, and the respective standard deviations are
also shown for all variables. The relative difference between the means of
the actual values and the simulated ones indicates whether the model
presented values larger than the real ones (positive) or smaller
(negative).
PARi and PARdif variables agreed over the three sites, when
comparing the actual and simulated values. The average of the modeled
PARi values, without aerosol, was 12.4 % higher than the average of
the real values for the Bananal Island site, 29.8 % higher for Rebio
Jaru, and 26.3 % higher for Sinop. On the other hand, the average of
the model values of PARdif was 35.9 % lower than the average of the
real values for Bananal Island, 52.0 % lower for Rebio Jaru, and 38.0 %
lower for Sinop.
Temperature also showed agreement regarding the increase in the mean values
modeled in relation to the mean real values. At Bananal Island, the
increase was 11.3 %, in Rebio Jaru it was 7.4 %, and in Sinop the relative
difference was 1.7 %. The relative difference between the actual and
modeled mean values of VPD and u* did not show agreement for the three
experimental sites. However, Bananal Island presented a significant increase
in VPD (∼50 %) for an atmosphere with no aerosols.
As demonstrated by Doughty et al. (2010) for a tropical forest, there is a
decrease of approximately 13 % in the FCO2 for every
1 ∘C increase in temperature, when it is above
28 ∘C. Due to the high correlation between VPD and T, the
results of the model without aerosols agree with the authors.
For FCO2, Bananal Island and Rebio Jaru presented higher mean modeled
values; i.e., a lower exchange of CO2 with the atmosphere is expected
under the effect of episodes without aerosols. Sinop presented the opposite
effect. This is mainly due to the differences in structural local
characteristics of each site. The response of photosynthetic capacity to an
increase in diffuse radiation may vary between different ecosystems and
seems to be related to the different properties of canopies (Zhang et al.,
2010; Kanniah et al., 2012; Tang and Dubayah, 2017). Niyogi (2004) showed
that carbon assimilation grows with an increase in aerosol concentration in
the case of forests and cultivated areas and decreases over pastures.
PARdif (%) (a) and PARi (Wm-2) (b) versus
SZA (∘), for Rebio Jaru. The observed values of PARi
and PARdif – obtained by the method of Reindl (1990) – are represented by
open circles, while the values calculated through libRadtran with AOD=0 are represented
by the black dots. The curves fitted to the datasets are shown in red.
Mean values with standard deviations of the turbulent fluxes of
CO2 (FCO2), sensible heat (H), and latent heat (LE), in µmol CO2 per square meter per second and watts per square meter, respectively. These values were
used as input parameters for building ANNs (measured), mean values and
standard deviations generated from the aerosol free scenarios (modeled),
and relative differences (%) between the measured and the modeled values
for all three study sites.
Study siteVariablesMeasuredModeledRelative differenceBananal Island – TOFCO2-7±5-6±1+12%H (Wm-2)131±67347±112+62%LE (Wm-2)287±108306±26+6%Rebio Jaru – ROFCO2-12±7-6±0+55%H (Wm-2)117±74184±65+36%LE (Wm-2)215±98219±68+2%Sinop – MTFCO2-18±7-20±3-12%H (Wm-2)137±71162±24+15%LE (Wm-2)227±89262±38+13%
The surrounding areas of the flux tower in Sinop show several regions of
pasture, which later became soy or maize plantations. For this reason, the
less complex canopy structures in the region (with lower vegetation and more
homogeneity in the vertical and horizontal distribution) have opposite
responses to that proposed by the “diffuse fertilization effect” theory. The
complexity of the canopies at Bananal Island and Rebio Jaru confirms the
effect.
The same type of test was performed in order to evaluate the relative
differences in H and LE under conditions of an observed aerosol
concentration and a simulated atmosphere without aerosols, with AOD550nm =0.0. In this test, energy (H and LE) fluxes were obtained using variables
calculated by the models (libRadtran and ANNs), except for the values of
AOD550nm (0.0) and SZAs, which were obtained through the original data.
Table 3 shows the average real values and the standard deviations
of the fluxes, H and LE, for the observed database, the average model values, and the relative difference between the means of the real values and the
simulated ones.
Modeled FCO2 (µmol CO2 m-2 s-1), H
(W m-2), and LE (W m-2) via ANNs versus
AOD550nm for three SZAs (15, 30, and
45∘) for three study sites. Values of temperature, VPD,
and u* were statistically modeled with ANNs built from meteorological data.
PARi and PARdif were obtained through libRadtran.
For all three sites, the values of sensible and latent heat fluxes
calculated through the ANNs for a scenario without aerosols were higher than
the mean observed values. It was observed that, for all experimental sites,
an aerosol-free atmosphere was associated with higher PARi and lower
PARdif values, which means that in an aerosol-free atmosphere, there
are conditions for more available energy at the surface, which may explain
the increase in the mean values of sensible and latent heat fluxes. The
higher availability of surface radiative energy was used for the
increase in temperature. The analysis of energy fluxes indicates that in the
presence of aerosols, there is a total reduction in both heat and water
exchange between the surface and the atmosphere over the evaluated sites.
Over all experimental sites, sensible heat flux was shown to be more
susceptible to changes in AOD550nm compared to latent heat flux. This fact
is greatly observed at Bananal Island and Rebio Jaru, but less in Sinop. A
possible explanation for this phenomenon can be obtained by evaluating the
absorption spectrum of water. The spectral region most affected by the
presence of biomass burning aerosols is the PAR, and evaluations were limited
to this spectrum only. This spectral region interacts very little with
water. Another possible explanation is due to the temperature rise in the
canopy itself. The vegetation responds to temperature increase with a
stomatal closure, which decreases evapotranspiration. Aerosols are more
efficient in affecting the exchange of heat between the atmosphere and the
vegetation than the exchange of moisture between the two.
Conclusions
In this study, we evaluated the impact of AOD550nm on land fluxes of
CO2, sensible heat, and latent heat for three study sites located in
the deforestation arch region of the Brazilian Amazon using a combination of
ANNs and radiative transfer modeling.
Aerosols were responsible for changing the amount of solar radiation
available at the surface and the partition between direct and diffuse
radiation. The radiative impact of aerosols caused variations in the
temperature and humidity as well as in FCO2. For the impact of aerosol
on the flux of CO2, the modeled values with ANN for Rebio Jaru
indicated a strong dependence on solar zenith angle. Temperature and VPD
were important in explaining FCO2 variance for all study sites.
For Bananal Island and Rebio Jaru, the increase in AOD550nm initially
generates a decrease in the absolute values of FCO2 to AOD550nm
∼0.25. However, for higher values of AOD550nm, FCO2
decreased, which indicates higher photosynthetic rates. In Sinop this
behavior was not observed.
We developed a group of ANNs relating meteorological and radiative variables
for three sites in the Amazon region (Fig. 8). For Bananal Island,
the behavior of decreasing FCO2 with the increase in AOD550nm remained.
For Rebio Jaru, FCO2 decreases with AOD550nm up to approximately
AOD550nm =0.50 and showed the opposite behavior after that. For Sinop,
the behavior between both scenarios remained basically the same.
In order to obtain the total radiative impact of aerosols on FCO2, a
third test consisted of a study to define the relative differences in
FCO2 between conditions in the presence of aerosol and without aerosols
(AOD550nm =0.0) in a combination of two distinct methodologies: ANNs and
radiative transfer modeling. The average of the modeled PARi values
without aerosol was higher than the average of the real values for all
sites, while the average of modeled values of PARdif was lower, as
expected. Temperature also increased in the modeled scenario without
aerosols in relation to the one containing aerosols. The relative difference
between the actual and modeled mean values of VPD and u* did not show
agreement for the three experimental sites.
Bananal Island and Rebio Jaru presented higher values of modeled CO2
flux in the scenario without aerosol, while Sinop presented the opposite
result. This was due to the canopy structural properties of Sinop and
surroundings presenting several crop fields. For this reason, the less
complex canopy architecture in the region acted to decrease the photosynthetic
response of the vegetation to less total incident PAR and more radiation in
its diffuse form. We conclude that in the absence of aerosols, CO2 flux
from the atmosphere to the surface in Rebio Jaru could be reduced to more
than half its current values in the presence of aerosols. This effect is
only observable by taking secondary effects into account.
The same type of evaluation was performed for the fluxes of sensible heat
and latent heat using ANNs. When evaluating the variables that best explain
the variance of sensible heat flux for Bananal Island, VPD and T showed a
strong influence of 27.4 % and 23.7 %, respectively. This fact indicates
that energy fluxes of Bananal Island are influenced by temperature. In the
case of Rebio Jaru, PARi explained about 17 % of the variance of H.
For Sinop, there is no favorable variable explaining the variances of H and
LE. However, friction velocity (u*) presented the highest index of
sensitivity (15 %), which indicates that the variance of both energy
fluxes was more influenced by turbulence itself.
In the first test, the behavior of the fluxes with AOD550nm showed similarities in all three sites. The average behavior of both H and LE decreased with an increase in aerosol optical depth. The increase in aerosol
concentration decreases total incident radiative energy at the surface and
thus generates a systematic decrease in H and LE. In the second test, the
variation in other meteorological variables affected the fluxes and,
although differences were present, the general behavior of energy fluxes
with aerosol optical depth remained. Sensible heat flux and latent heat flux
decreased with higher AOD550nm.
In the scenario with AOD550nm =0.0, the values of sensible and latent
heat fluxes were higher than the observed values. This indicates that in
an atmosphere without aerosols, more energy is available at the surface.
Sensible heat flux was shown to be more susceptible to changes in AOD550nm
compared to latent heat flux. This fact is reported in the Bananal Island
and Rebio Jaru datasets, but less in Sinop. The radiative spectral region
most affected by the presence of the biomass burning aerosols is the PAR,
and only the aerosol optical depth at 550 nm was evaluated. The radiation at
550 nm interacts little with water vapor. Also, an increase in canopy
temperature may lead to stomatal closure decreasing total
evapotranspiration, which can impact latent heat flux negatively.
The two groups of ANNs developed in this study, i.e., characterizing the
first- and second-order radiative impact of aerosols on mass and energy
fluxes, can be used for flux data gap filling, as well as modeling and
evaluating future climate. More evaluations of the impact of aerosols on
energy fluxes throughout the Amazon basin are needed, and machine learning
techniques are an efficient and accessible tool to develop these studies.
It is important to highlight some important caveats of the ANN methodology
used to drawn our conclusions, including, but not limited to, general
extrapolations; for example, a limited number of sites were used in this study; there is a high dependence on sampling together with other data biases, e.g., only the dry
season used here; and there was the non-consideration of all
confounding factors of the examined problem – e.g., a limited number of input
variables (seven) were used to build the ANNs throughout these experiments. Yet,
using restricted datasets and objective choices of features to study rather
than an extensive and generic approach remains valid and important. Moreover,
expert intervention associated with best practices is expected to diminish
these weaknesses associated with machine learning techniques (Reichstein et
al., 2019).
Future efforts must include ways to go around the main caveats of this work,
including the following: (i) the addition of multi-source (more field sites), multi-scale
(the whole Amazon basin represented at different resolutions), and complex
spatiotemporal relations (intra- and inter-seasonal variability of flux
data, as well as advection of aerosols from different sources); (ii) uncertainty estimation should be integrated into models; (iii) using
physically based models to test the statistical ones, e.g., linking radiative
transfer and ecophysiology models to evaluate the theoretical first-order
impact of aerosols on light scattering and photosynthesis. Deep learning
techniques such as recurrent neural networks (Schmidhuber, 2015) or
long short-term memories (Hochreiter and Schmidhuber, 1997) may be able to
contour some of these weaknesses (Reichstein et al., 2019).
Nevertheless, coupling machine learning techniques at local and regional
levels with land surface models (Moreira et al., 2017) and Earth system
models (Malavelle et al., 2019) can improve the representation of poorly
known process-based mechanisms or add information about unknown
relationships between meteorological variables and surface fluxes.
Code and data availability
Flux and aerosol data used in this study are available at
10.6084/m9.figshare.8239691.v1 (Braghiere et al., 2019).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-20-3439-2020-supplement.
Author contributions
RKB designed the study, conducted the analysis, and wrote the paper
with help from all the other authors. MAY coordinated the intensive field
campaign at Rebio Jaru in the dry season of 2007 and contributed to the
writing of the paper. NMEdR developed the aerosols optical models.
HRdR, JdSN, and ACdA coordinated the measurements and shared the flux
data for Bananal Island, Sinop, and Rebio Jaru, respectively.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
This research was carried out in part at the Jet
Propulsion Laboratory, California Institute of Technology, under a contract
with the National Aeronautics and Space Administration. The authors would like to thank the staff from ICMBio at
Rebio Jaru. Renata Aguiar, Federal University of Rondônia, Campus Ji
Paraná, and the LBA local office for collecting and processing the flux
data. Also, thanks to Frederico Trindade Teófilo and students of the LBA local office, as
well as to Ruth Souza and other employees for the support during the
field campaign. The authors thank three anonymous reviewers, whose comments improved the paper.
Financial support
This work was partly funded by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001, at the University of São Paulo, Brazil. Marcia Akemi Yamasoe acknowledges FAPESP (grant number 06-56550-5) and the National Council for Scientific and Technological Development – Brasil (CNPq) for grant number 313005/2018-4.
Review statement
This paper was edited by Janne Rinne and reviewed by three anonymous referees.
References
Araújo Junior, G. J. L. D. de, Pereira, J. A. R., Silva, T. M. V. da,
Mafra, H., Brass, J. A., Lockwood, R. N., Higgins, R. G., and Riggan, P. J.:
Utilização do sensor AIRDAS (Airborne Infrared Disaster Assessment
System) no monitoramento de desflorestamentos no norte do Estado do Mato
Grosso – Brasil, in: Simpósio Brasileiro de Sensoriamento Remoto, 9
(SBSR), edited by: Krug, T., Rudorff, B. F. T., and de Freitas, U. M.,
1433–1442, INPE, São José dos Campos, 1998.Bayram, S., Ocal, M. E., Laptali Oral, E., and Atis, C. D.: Comparison of
multi layer perceptron (MLP) and radial basis function (RBF) for
construction cost estimation: the case of Turkey, J. Civ. Eng. Manag., 22, 480–490, 10.3846/13923730.2014.897988, 2016.Black, K., Davis, P., Lynch, P., Jones, M., McGettigan, M., and Osborne, B.:
Long-term trends in solar irradiance in Ireland and their potential effects
on gross primary productivity, Agr. Forest Meteorol., 141, 118–132, 10.1016/j.agrformet.2006.09.005, 2006.Borma, L. S., da Rocha, H. R., Cabral, O. M., von Randow, C., Collicchio,
E., Kurzatkowski, D., Brugger, P. J., Freitas, H., Tannus, R., Oliveira, L.,
Rennó, C. D., and Artaxo, P.: Atmosphere and hydrological controls of the
evapotranspiration over a floodplain forest in the Bananal Island region,
Amazonia, J. Geophys. Res., 114, G01003,
10.1029/2007JG000641, 2009.Bosveld, F. C. and Beljaars, A. C. M.: The impact of sampling rate on
eddy-covariance flux estimates, Agr. Forest Meteorol., 109, 39–45,
10.1016/S0168-1923(01)00257-X, 2001.Boucher, O., Randall, D., Artaxo, P., Bretherton, C., Feingold, G., Forster,
P., Kerminen, V.-M. V.-M., Kondo, Y., Liao, H., Lohmann, U., Rasch, P.,
Satheesh, S. K., Sherwood, S., Stevens, B., Zhang, X. Y., and Zhan, X. Y.:
Clouds and Aerosols, Clim. Chang. 2013 Phys. Sci. Basis. Contrib. Work. Gr.
I to Fifth Assess. Rep. Intergov. Panel Clim. Chang.,
10.1017/CBO9781107415324.016, 2013.Buckland, C. E., Bailey, R. M., and Thomas, D. S. G.: Using artificial neural
networks to predict future dryland responses to human and climate
disturbances, Sci. Rep., 9, 3855, 10.1038/s41598-019-40429-5, 2019.Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N.,
Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima, T.: Tropospheric
Aerosol Optical Thickness from the GOCART Model and Comparisons with
Satellite and Sun Photometer Measurements, J. Atmos. Sci., 59, 461–483, 10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2, 2002.do Rosário, N. E., Yamasoe, M. A., and Longo, K. M.: Aerosol Optical
Depth and Ångström Coefficient retrievals over the Amazon Forest
during 2007 biomass burning season, 1100, 494–497,
10.1063/1.3117029, 2009.do Rosário, N. E., Yamasoe, M. A., Brindley, H., Eck, T. F., and Schafer,
J.: Downwelling solar irradiance in the biomass burning region of the
southern Amazon: Dependence on aerosol intensive optical properties and role
of water vapor, J. Geophys. Res.-Atmos., 116, D18304,
10.1029/2011JD015956, 2011.Doughty, C. E., Flanner, M. G., and Goulden, M. L.: Effect of smoke on
subcanopy shaded light, canopy temperature, and carbon dioxide uptake in an
Amazon rainforest, Global Biogeochem. Cy., 24, 10.1029/2009GB003670, 2010.Ezhova, E., Ylivinkka, I., Kuusk, J., Komsaare, K., Vana, M., Krasnova, A., Noe, S., Arshinov, M., Belan, B., Park, S.-B., Lavrič, J. V., Heimann, M., Petäjä, T., Vesala, T., Mammarella, I., Kolari, P., Bäck, J., Rannik, Ü., Kerminen, V.-M., and Kulmala, M.: Direct effect of aerosols on solar radiation and gross primary production in boreal and hemiboreal forests, Atmos. Chem. Phys., 18, 17863–17881, 10.5194/acp-18-17863-2018, 2018.Fletcher, C. G., Kravitz, B., and Badawy, B.: Quantifying uncertainty from aerosol and atmospheric parameters and their impact on climate sensitivity, Atmos. Chem. Phys., 18, 17529–17543, 10.5194/acp-18-17529-2018, 2018.
Forster, P., Ramaswamy, V., Artaxo, P., Berntsen, T., Betts, R., Fahey, D.
W., Haywood, J., Lean, J., Lowe, D. C., Myhre, G., Nganga, J., Prinn, R.,
Raga, G., Schulz, M., and Van Dorland, R.: Changes in Atmospheric
Constituents and in Radiative Forcing, in Climate Change 2007: The Physical
Science Basis. Contribution of Working Group I to the Fourth Assessment
Report of the Intergovernmental Panel on Climate Change, 2007.Gentine, P., Pritchard, M., Rasp, S., Reinaudi, G., and Yacalis, G.: Could
Machine Learning Break the Convection Parameterization Deadlock?, Geophys.
Res. Lett., 45, 5742–5751, 10.1029/2018GL078202, 2018.
Gomes, J. B.: Conversão de florestas tropicais em sistemas pecuários na Amazônia: quais são as implicações no microclima da região?, Universidade Federal de Rondônia, 2011.Ghosh, S. and Mujumdar, P. P.: Statistical downscaling of GCM simulations to
streamflow using relevance vector machine, Adv. Water Resour., 31,
132–146, 10.1016/j.advwatres.2007.07.005, 2008.Goyal, M. K., Bharti, B., Quilty, J., Adamowski, J., and Pandey, A.: Modeling
of daily pan evaporation in sub tropical climates using ANN, LS-SVR, Fuzzy
Logic, and ANFIS, Expert Syst. Appl., 41, 5267–5276,
10.1016/j.eswa.2014.02.047, 2014.Gu, L., Baldocchi, D., Verma, S. B., Black, T. A., Vesala, T., Falge, E. M.,
and Dowty, P. R.: Advantages of diffuse radiation for terrestrial ecosystem
productivity, J. Geophys. Res.-Atmos., 107, ACL 2-1–ACL 2-23,
10.1029/2001JD001242, 2002.He, J., Glotfelty, T., Yahya, K., Alapaty, K., and Yu, S.: Does temperature
nudging overwhelm aerosol radiative effects in regional integrated climate
models?, Atmos. Environ., 154, 42–52, 10.1016/j.atmosenv.2017.01.040,
2017.Henyey, L. C. and Greenstein, J. L.: Diffuse radiation in the Galaxy,
Astrophys. J., 93, 70, 10.1086/144246, 1941.Hochreiter, S. and Schmidhuber, J.: Long Short-Term Memory, Neural Comput.,
9, 1735–1780, 10.1162/neco.1997.9.8.1735, 1997.Hubanks, P. A.: MODIS Atmosphere QA Plan for Collection 005, 67, available at: https://atmosphere-imager.gsfc.nasa.gov/sites/default/files/ModAtmo/QA_Plan_C61_Master_2017_03_15.pdf, last access: 16 March 2020, 2017.Huntingford, C., Jeffers, E. S., Bonsall, M. B., Christensen, H. M., Lees,
T., and Yang, H.: Machine learning and artificial intelligence to aid climate
change research and preparedness, Environ. Res. Lett., 14, 124007,
10.1088/1748-9326/ab4e55, 2019.IBAMA: Plano de prevenção e combate aos incêndios florestais da Reserva Biologógica do Jaru (2006–2007), available at: http://piwik.ibama.gov.br/phocadownload/prevfogo/planos_operativos/plano_operativo_reserva_biolgica_do_jaru.pdf, last access: 16 March 2020, 2006.Jung, M., Reichstein, M., Ciais, P., Seneviratne, S. I., Sheffield, J.,
Goulden, M. L., Bonan, G., Cescatti, A., Chen, J., de Jeu, R., Dolman, A.
J., Eugster, W., Gerten, D., Gianelle, D., Gobron, N., Heinke, J., Kimball,
J., Law, B. E., Montagnani, L., Mu, Q., Mueller, B., Oleson, K., Papale, D.,
Richardson, A. D., Roupsard, O., Running, S., Tomelleri, E., Viovy, N.,
Weber, U., Williams, C., Wood, E., Zaehle, S., and Zhang, K.: Recent decline
in the global land evapotranspiration trend due to limited moisture supply,
Nature, 467, 951–954, 10.1038/nature09396, 2010.Kanniah, K. D., Beringer, J., Tapper, N. J., and Long, C. N.: Aerosols and
their influence on radiation partitioning and savanna productivity in
northern Australia, Theor. Appl. Climatol., 100, 423–438,
10.1007/s00704-009-0192-z, 2010.Kanniah, K. D., Beringer, J., North, P., and Hutley, L.: Control of atmospheric particles on diffuse radiation and terrestrial plant productivity, Prog. Phys. Geogr. Earth Environ., 36, 209–237, 10.1177/0309133311434244, 2012.Kanniah, K. D., Beringer, J., and Hutley, L.: Exploring the link between
clouds, radiation, and canopy productivity of tropical savannas, Agr. Forest Meteorol., 182–183, 304–313, 10.1016/j.agrformet.2013.06.010, 2013.Kiehl, J. T. and Trenberth, K. E.: Earth's Annual Global Mean Energy Budget, B. Am. Meteorol. Soc., 78, 197–208, 10.1175/1520-0477(1997)078<0197:EAGMEB>2.0.CO;2, 1997.Krasnopolsky, V. M., Fox-Rabinovitz, M. S., and Chalikov, D. V.: New Approach
to Calculation of Atmospheric Model Physics: Accurate and Fast Neural
Network Emulation of Longwave Radiation in a Climate Model, Mon. Weather
Rev., 133, 1370–1383, 10.1175/MWR2923.1, 2005.Lee, M. S., Hollinger, D. Y., Keenan, T. F., Ouimette, A. P., Ollinger, S. V., and Richardson, A. D.: Model-based analysis of the impact of diffuse radiation on CO2 exchange in a temperate deciduous forest, Agric. For. Meteorol., 249, 377–389, 10.1016/j.agrformet.2017.11.016, 2018.Levy, R. C., Leptoukh, G. G., Kahn, R., Zubko, V., Gopalan, A., and Remer, L.
A.: A critical look at deriving monthly aerosol optical depth from satellite
data, IEEE T. Geosci. Remote, 47, 2942–2956,
10.1109/TGRS.2009.2013842, 2009.Li, T., Heuvelink, E., Dueck, T. A., Janse, J., Gort, G., and Marcelis, L. F.
M.: Enhancement of crop photosynthesis by diffuse light: quantifying the
contributing factors, Ann. Bot., 114, 145–156, 10.1093/aob/mcu071,
2014.Loveland, T. R. and Belward, A. S.: The IGBP-DIS global 1km land cover data
set, DISCover: First results, Int. J. Remote Sens., 18, 3289–3295,
10.1080/014311697217099, 1997.Malavelle, F. F., Haywood, J. M., Mercado, L. M., Folberth, G. A., Bellouin, N., Sitch, S., and Artaxo, P.: Studying the impact of biomass burning aerosol radiative and climate effects on the Amazon rainforest productivity with an Earth system model, Atmos. Chem. Phys., 19, 1301–1326, 10.5194/acp-19-1301-2019, 2019.Mayer, B. and Kylling, A.: Technical note: The libRadtran software package for radiative transfer calculations – description and examples of use, Atmos. Chem. Phys., 5, 1855–1877, 10.5194/acp-5-1855-2005, 2005.Mayer, B., Hoch, S. W., and Whiteman, C. D.: Validating the MYSTIC three-dimensional radiative transfer model with observations from the complex topography of Arizona's Meteor Crater, Atmos. Chem. Phys., 10, 8685–8696, 10.5194/acp-10-8685-2010, 2010.McGovern, A., Elmore, K. L., Gagne, D. J., Haupt, S. E., Karstens, C. D.,
Lagerquist, R., Smith, T., and Williams, J. K.: Using Artificial Intelligence
to Improve Real-Time Decision-Making for High-Impact Weather, B. Am.
Meteorol. Soc., 98, 2073–2090, 10.1175/BAMS-D-16-0123.1, 2017.Mercado, L. M., Bellouin, N., Sitch, S., Boucher, O., Huntingford, C., Wild,
M., and Cox, P. M.: Impact of changes in diffuse radiation on the global land
carbon sink, Nature, 458, 1014–1017, 10.1038/nature07949, 2009.Moreira, D. S., Longo, K. M., Freitas, S. R., Yamasoe, M. A., Mercado, L. M., Rosário, N. E., Gloor, E., Viana, R. S. M., Miller, J. B., Gatti, L. V., Wiedemann, K. T., Domingues, L. K. G., and Correia, C. C. S.: Modeling the radiative effects of biomass burning aerosols on carbon fluxes in the Amazon region, Atmos. Chem. Phys., 17, 14785–14810, 10.5194/acp-17-14785-2017, 2017.Nguyen, P., Ombadi, M., Sorooshian, S., Hsu, K., AghaKouchak, A., Braithwaite, D., Ashouri, H., and Thorstensen, A. R.: The PERSIANN family of global satellite precipitation data: a review and evaluation of products, Hydrol. Earth Syst. Sci., 22, 5801–5816, 10.5194/hess-22-5801-2018, 2018.Niyogi, D., Chang, H. I., Saxena, V. K., Holt, T., Alapaty, K., Booker, F.,
Chen, F., Davis, K. J., Holben, B., Matsui, T., Meyers, T., Oechel, W. C.,
Pielke, R. A., Wells, R., Wilson, K., and Xue, Y.: Direct observations of the
effects of aerosol loading on net ecosystem CO2 exchanges over different
landscapes, Geophys. Res. Lett., 31, L20506, 10.1029/2004GL020915, 2004.Oliveira, P. H. F., Artaxo, P., Pires, C., De Lucca, S., Procópio, A.,
Holben, B., Schafer, J., Cardoso, L. F., Wofsy, S. C., and Rocha, H. R.: The
effects of biomass burning aerosols and clouds on the CO2 flux in
Amazonia, Tellus B, 59,
338–349, 2007.O'Sullivan, M., Rap, A., Reddington, C. L., Spracklen, D. V., Gloor, M., and
Buermann, W.: Small global effect on terrestrial net primary production due
to increased fossil fuel aerosol emissions from East Asia since the turn of
the century, Geophys. Res. Lett., 43, 8060–8067,
10.1002/2016GL068965, 2016.Papale, D. and Valentini, R.: A new assessment of European forests carbon
exchanges by eddy fluxes and artificial neural network spatialization, Glob.
Change Biol., 9, 525–535, 10.1046/j.1365-2486.2003.00609.x, 2003.Persad, G. G. and Caldeira, K.: Divergent global-scale temperature effects
from identical aerosols emitted in different regions, Nat. Commun., 9,
3289, 10.1038/s41467-018-05838-6, 2018.Pierluissi, J. H. and Peng, G.-S.: New Molecular Transmission Band Models
For LOWTRAN, Opt. Eng., 24, 10.1117/12.7973523, 1985.Rap, A., Spracklen, D. V., Mercado, L., Reddington, C. L., Haywood, J. M.,
Ellis, R. J., Phillips, O. L., Artaxo, P., Bonal, D., Restrepo Coupe, N., and
Butt, N.: Fires increase Amazon forest productivity through increases in
diffuse radiation, Geophys. Res. Lett., 42, 4654–4662,
10.1002/2015GL063719, 2015.Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J.,
Carvalhais, N., and Prabhat: Deep learning and process understanding for
data-driven Earth system science, Nature, 566, 195–204,
10.1038/s41586-019-0912-1, 2019.Reindl, D. T., Beckman, W. A., and Duffie, J. A.: Evaluation of hourly tilted
surface radiation models, Sol. Energy, 45, 9–17,
10.1016/0038-092X(90)90061-G, 1990.Ricchiazzi, P., Yang, S., Gautier, C., and Sowle, D.: SBDART: A Research and
Teaching Software Tool for Plane-Parallel Radiative Transfer in the Earth's
Atmosphere, B. Am. Meteorol. Soc., 79, 2101–2114,
10.1175/1520-0477(1998)079<2101:SARATS>2.0.CO;2, 1998.Roderick, M. L., Farquhar, G. D., Berry, S. L., and Noble, I. R.: On the
direct effect of clouds and atmospheric particles on the productivity and
structure of vegetation, Oecologia, 129, 21–30,
10.1007/s004420100760, 2001.Rodriguez-Galiano, V. F., Ghimire, B., Rogan, J., Chica-Olmo, M., and
Rigol-Sanchez, J. P.: An assessment of the effectiveness of a random forest
classifier for land-cover classification, ISPRS J. Photogramm. Remote Sens.,
67, 93–104, 10.1016/j.isprsjprs.2011.11.002, 2012.Rojek, I.: Technological process planning by the use of neural networks,
Artif. Intell. Eng. Des. Anal. Manuf., 31, 1–15,
10.1017/S0890060416000147, 2017.Schmidhuber, J.: Deep learning in neural networks: An overview, Neural
Networks, 61, 85–117, 10.1016/j.neunet.2014.09.003, 2015.Schneider, T., Lan, S., Stuart, A., and Teixeira, J.: Earth System Modeling
2.0: A Blueprint for Models That Learn From Observations and Targeted
High-Resolution Simulations, Geophys. Res. Lett., 44, 12396–12417,
10.1002/2017GL076101, 2017.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From
Air Pollution to Climate Change, New Jersey, John Wiley & Sons, inc., 1225 p., Senna, 2006.Song, X.-P., Huang, C., Saatchi, S. S., Hansen, M. C., and Townshend, J. R.:
Annual Carbon Emissions from Deforestation in the Amazon Basin between 2000
and 2010, edited by B. Hérault, PLoS One, 10, e0126754,
10.1371/journal.pone.0126754, 2015.Stanhill, G. and Cohen, S.: Global dimming: a review of the evidence for a
widespread and significant reduction in global radiation with discussion of
its probable causes and possible agricultural consequences, Agr. Forest Meteorol., 107, 255–278, 10.1016/S0168-1923(00)00241-0,
2001.Steiner, A. L., Mermelstein, D., Cheng, S. J., Twine, T. E., and Oliphant,
A.: Observed Impact of Atmospheric Aerosols on the Surface Energy Budget,
Earth Interact., 17, 1–22, 10.1175/2013EI000523.1, 2013.Tang, H. and Dubayah, R.: Light-driven growth in Amazon evergreen forests
explained by seasonal variations of vertical canopy structure, P. Natl.
Acad. Sci. USA, 114, 2640–2644, 10.1073/pnas.1616943114, 2017.Textor, C., Schulz, M., Guibert, S., Kinne, S., Balkanski, Y., Bauer, S., Berntsen, T., Berglen, T., Boucher, O., Chin, M., Dentener, F., Diehl, T., Easter, R., Feichter, H., Fillmore, D., Ghan, S., Ginoux, P., Gong, S., Grini, A., Hendricks, J., Horowitz, L., Huang, P., Isaksen, I., Iversen, I., Kloster, S., Koch, D., Kirkevåg, A., Kristjansson, J. E., Krol, M., Lauer, A., Lamarque, J. F., Liu, X., Montanaro, V., Myhre, G., Penner, J., Pitari, G., Reddy, S., Seland, Ø., Stier, P., Takemura, T., and Tie, X.: Analysis and quantification of the diversities of aerosol life cycles within AeroCom, Atmos. Chem. Phys., 6, 1777–1813, 10.5194/acp-6-1777-2006, 2006.Trenberth, K. E., Fasullo, J. T., and Kiehl, J.: Earth's Global Energy Budget, B. Am. Meteorol. Soc., 90, 311–324, 10.1175/2008BAMS2634.1, 2009.Unger, N., Yue, X., and Harper, K. L.: Aerosol climate change effects on land
ecosystem services, Faraday Discuss., 200, 121–142, 10.1039/C7FD00033B,
2017.Vourlitis, G. L., De Souza Nogueira, J., De Almeida Lobo, F., Sendall, K.
M., De Paulo, S. R., Antunes Dias, C. A., Pinto, O. B., and De Andrade, N. L.
R.: Energy balance and canopy conductance of a tropical semi-deciduous
forest of the southern Amazon Basin, Water Resour. Res., 44, 10.1029/2006WR005526, 2008.Wang, Y., Wang, M., Zhang, R., Ghan, S. J., Lin, Y., Hu, J., Pan, B., Levy,
M., Jiang, J. H., and Molina, M. J.: Assessing the effects of anthropogenic
aerosols on Pacific storm track using a multiscale global climate model,
P. Natl. Acad. Sci. USA, 111, 6894–6899, 10.1073/pnas.1403364111,
2014.Watson-Parris, D., Sutherland, S., Christensen, M., Caterini, A.,
Sejdinovic, D., and Stier, P.: Detecting anthropogenic cloud perturbations
with deep learning, available at:
http://arxiv.org/abs/1911.13061 (last access: 12 March 2020), 2019.Whitley, R. J., Macinnis-Ng, C. M. O., Hutley, L. B., Beringer, J., Zeppel,
M., Williams, M., Taylor, D., and Eamus, D.: Is productivity of mesic
savannas light limited or water limited? Results of a simulation study,
Glob. Change Biol., 17, 3130–3149, 10.1111/j.1365-2486.2011.02425.x, 2011.Yamasoe, M. A., von Randow, C., Manzi, A. O., Schafer, J. S., Eck, T. F., and Holben, B. N.: Effect of smoke and clouds on the transmissivity of photosynthetically active radiation inside the canopy, Atmos. Chem. Phys., 6, 1645–1656, 10.5194/acp-6-1645-2006, 2006.Yang, H., Piao, S., Huntingford, C., Peng, S., Ciais, P., Chen, A., Zhou,
G., Wang, X., Gao, M., and Zscheischler, J.: Strong but Intermittent Spatial
Covariations in Tropical Land Temperature, Geophys. Res. Lett., 46,
356–364, 10.1029/2018GL080463, 2019.
Yang, Y., Guan, H., Batelaan, O., McVicar, T. R., Long, D., Piao, S., Liang,
W., Liu, B., Jin, Z., and Simmons, C. T.: Contrasting responses of water use
efficiency to drought across global terrestrial ecosystems, Sci. Rep., 6,
23284, 10.1038/srep23284, 2016.Wu, C., Chen, Y., Peng, C., Li, Z., and Hong, X.: Modeling and estimating
aboveground biomass of Dacrydium pierrei in China using machine learning
with climate change, J. Environ. Manage., 234, 167–179,
10.1016/j.jenvman.2018.12.090, 2019.Zhang, M., Yu, G.-R., Zhang, L.-M., Sun, X.-M., Wen, X.-F., Han, S.-J., and Yan, J.-H.: Impact of cloudiness on net ecosystem exchange of carbon dioxide in different types of forest ecosystems in China, Biogeosciences, 7, 711–722, 10.5194/bg-7-711-2010, 2010.