Introduction
The impact of the atmospheric increase in greenhouse gases (GHGs) on climate
change is well known (IPCC, 2013). GHG emissions, due to natural as well as
anthropogenic sources, are currently estimated and reported by each national
agency to the United Nations Framework Convention on Climate Change (UNFCCC).
A better understanding of the underlying processes causing these emissions
can help in the implementation of future emission reduction strategies.
Methane (CH4) is the second most important anthropogenic GHG that is
covered by the UNFCCC. The atmospheric mixing ratio of CH4 has
substantially changed since pre-industrial times from a global average of
715 nmol mol-1 to more than 1774 nmol mol-1 (IPCC, 2013).
Nowadays, the contribution of CH4 related to anthropogenic activities in
the atmosphere represents about 25 % of total additional anthropogenic
radiative forcing (IPCC, 2013). However, CH4 has a relatively short
lifetime in the atmosphere (∼ 9 years) and this makes it relevant in
defining immediate and efficient emission reduction strategies (Prinn et al.,
2000). Particularly in Spain, man-made methane emissions are mainly due to
enteric fermentation (38 %), management of manure (20 %) and
landfills (36 %) (WWF, 2014; MMA, 2016). The remaining methane
contributions in Spain are due to rice cultivation (e.g. Àgueda et al.,
2018), coal mining, leaks in natural gas infrastructures and waste water
treatment. The CH4 emission due to enteric fermentation related to
livestock is directly linked to the number of animals of each type or breed of cattle, their age, their diet
and environmental conditions (MMA, 2016). Spanish CH4 emissions due to
enteric fermentation were estimated to be 11 704 Gg CO2-eq
(MMA, 2016).
In order to estimate GHG emissions, bottom-up (based on fuel consumption and
anthropogenic activity data) and top-down methods (based on atmospheric
observations and modelling) are both widely applied and the scientific
community has focussed on reducing their related uncertainties and
understanding systematic inconsistencies (e.g. Vermeulen et al., 2006;
Bergamaschi et al., 2010; NRC, 2010; Jeong et al., 2013; Hiller et al.,
2014). Top-down methods usually require both high-quality and long-term GHG
observations. European projects, such as InGOS
(www.ingos-infrastructure.eu, last access: March 2018), and infrastructures, such as ICOS
(www.icos-infrastructure.eu, last access: April 2018), aim to offer atmospheric CO2 and non-CO2 GHG data and
data products to better understand GHG fluxes in Europe and adjacent
regions.
Unfortunately, in some European regions, such as Spain, there is still a
significant lack of high-quality atmospheric GHG observations. The Catalan
Institute of Climate Sciences (IC3) has been working since 2010 within the
ClimaDat project in setting up a network of stations in national parks for
continuous measurements of mixing ratios of GHGs, tracers and meteorological
parameters (www.climadat.es, last access: April 2018). The IC3 network mainly aims to monitor and study the exchange of
GHGs between the land surface and the lower atmosphere (troposphere) in
different ecosystems, which are characterized by different biogenic and
anthropogenic processes, under different synoptic conditions.
Besides GHG mixing ratios, co-located observations of additional gases can
provide us with useful tracers for source apportionment studies or to help us
to better understand atmospheric processes (e.g. Zahorowski et al., 2004).
The radioactive noble gas radon (222Rn), due to its chemical and
physical characteristics (e.g. Nazaroff and Nero, 1988), is being extensively
used for studying atmosphere dynamics, such as boundary layer evolution (e.g.
Galmarini, 2006; Vinuesa and Galmarini, 2007), and soil–atmosphere exchanges
(e.g. Schery et al., 1998; Zahorowski et al., 2004; Szegvary et al., 2009;
Grossi et al., 2012, 2016; Vargas et al., 2015). European GHG-monitoring
infrastructures already include atmospheric 222Rn monitors in their
stations (e.g. Arnold et al., 2010; Zimnoch et al., 2014; Schmithüsen et
al., 2017). The co-evolution of atmospheric 222Rn and GHG concentrations
can also be used within the radon tracer method (RTM) to estimate local and regional GHG
fluxes (e.g. Van der Laan et al., 2010; Levin et al., 2011; Vogel et al.,
2012; Wada et al., 2013; Grossi et al., 2014).
In this study we analysed the time series of atmospheric CH4 mixing
ratios measured at the IC3 station in Gredos and Iruelas (GIC3) between
January 2013 and December 2015. The main aim was to investigate the main
drivers that influence the daily and seasonal variability of methane
concentrations in this mountainous rural southern European region. The GIC3
station is located on the Spanish plateau, an area mainly characterized by
livestock activity and where transhumance is still practiced (Ruiz Perez and
Valero Sáez, 1990). This is an ancestral activity consisting of the
seasonal movement of livestock over long distances to reach warmer regions
during the winter together with a return to the mountains in summer where
pastures are greener and more suitable for grazing activities (Ruiz Perez and
Valero Sáez, 1990; López Sáez et al., 2009). The livestock leaves
the GIC3 region to go to southern Spanish regions during the cold period. The
enteric fermentation due to digestive processes in animals could thus be a
significant CH4 source in this area. The Unión de Pequeños
Agricultores (UPA, 2009) reports that between 2004 and 2009 an average of
800 000 transhumant animals were hosted in Spain and 40 000 (5 % of
total) were counted in the province of Ávila (extension: 8048 km2),
where the GIC3 station is located. According to the available literature, in
this area 85 % of livestock still performs transhumance, with
500 stockbreeders moving every winter from the Gredos national park (GNP)
to warmer areas of Spain, such as Extremadura (Ruiz Perez and Valero
Sáez, 1990; López Sáez et al., 2009; Libro Blanco, 2013).
Generally, this mobility of cattle and associated CH4 emissions (i.e. a
major regional CH4 source) cannot easily be included in country-wide
(annual) inventories because it has not yet been properly quantified and
reported by nations. The present study aims to highlight the utility of
222Rn as a tracer to retrieve independent GHG fluxes on a monthly basis
using atmospheric 222Rn and CH4 data. This work represents a first
step towards a better characterisation of transient sources, such as
transhumant livestock for CH4, which could help to improve national
emissions inventories. Finally, it offers new CH4 data for an
under-sampled area which will help in the improvement of the regional and
global methane budgets.
GIC3 is a new atmospheric station so its location, the surrounding region and
the instrumentation used at this station are described in the methodology
section of this paper. In the first part of the results section both the
daily and seasonal changes in CH4 mixing ratios observed at the GIC3
station have been analysed in relation to 222Rn and PBLH variability. In
the second part, the nocturnal CH4 fluxes and their monthly variability
have been estimated by the RTM, following Vogel et
al. (2012), and using an emission inventory for CH4 (EDGARv4.2). Both
flux estimation methods have been applied using the same source region as
modelled by the atmospheric transport model FLEXPARTv9.0.2. The possible
influence of large cities surrounding GIC3 and of seasonally changing
meteorological conditions on the retrieved CH4 fluxes has also been
investigated. Finally, the difference in CH4 fluxes between the cattle
season, when livestock is present in the GIC3 region, and the no-cattle
season, when the transhumant cattle have migrated to the south of Spain,
calculated using the RTM, has been estimated.
Methods
Study site: Gredos and Iruelas station (GIC3)
The Gredos and Iruelas station is located in a rural region of the
Spanish central plateau (40.35∘ N, 5.17∘ E; 1440 m above
sea level – a.s.l.), as shown in Fig. S1 of the Supplement. GIC3 is located
on the west side of the GNP, which has a total
extension of 86 397 ha. The mountains of the GNP form the highest
mountain range in the east–west-orientated central mountain system. The GNP has a,
predominantly, granitic basement and is thus covered by soil with high
activity levels of 228U (Nazaroff and Nero, 1988). The average
222Rn flux in this area is about 70–100 Bq m-2 h-1 (e.g.
López-Coto et al., 2013; Karstens et al., 2015), which is almost twice
the average radon flux in central Europe (Szegvary et al., 2009,
López-Coto et al., 2013; Grossi et al., 2016). The vegetation in the GIC3
area is stratified according to altitude and the main land use practice is a
mixture of agro-forestry exploitation (EEA, 2013)
Livestock farming is one of the main economic activities in the area around
the GIC3 station (Ruiz Perez and Valero Sáez, 1990; López Saéz et
al., 2009; MMA, 2016; Hernández, 2016). In the GNP the seasonal
migration of livestock starts between November and December, when they travel
to the south of the Iberian Peninsula, and they do not return until late
May–mid-June (Ruiz Perez and Valero Sáez, 1990). In Fig. S2, a map of
the main Spanish transhumant paths is presented. Unfortunately, no specific
reports of cattle mobility data are so far available for the GIC3 area.
Besides livestock activities, there are three small-sized to medium-sized
water reservoirs and four medium-sized to large cities in the wider area
surrounding GIC3. The water reservoirs as well as several facilities
present in the cities, e.g. landfills or waste water treatment plants,
represent CH4 sources which could also influence methane concentrations
observed at the GIC3 station under specific synoptic conditions. The water
reservoirs are located in the west and north-west area of GIC3: (i) The
Gabriel and Galan reservoir with an extension of 4683 ha (40.25∘ N;
-6.13∘ E; 80 km away from GIC3), (ii) Santa Teresa with an
extension of 2663 ha (40.60∘ N; -5.58∘ E; 42 km away
from GIC3), and (iii) Almendra with an extension of 7940 ha
(41.25∘ N; -6.26∘ E; 120 km away from GIC3). The
metropolitan area of Madrid, which comprises about 6.3 million inhabitants,
is situated approximately 120 km to the east of GIC3. Valladolid, located
150 km to the west of GIC3, is reported to have approximately 416 000
inhabitants, while smaller cities like Salamanca (84 km to the north-west)
and Ávila (55 km to the north-east) only have 229 000 and 59 000
inhabitants, respectively. More information about these four cities is
reported in Table S1 of the Supplement.
Atmospheric measurements of CH4 and 222Rn
Air sampling
Atmospheric CH4, CO2 and 222Rn concentrations have been
continuously measured since November 2012 at the GIC3 station (air inlet
20 m above ground level (a.g.l.) tower). CH4 and CO2 are measured
with a frequency of 0.2 Hz using a G2301 analyser (Picarro Inc., USA).
Hourly atmospheric 222Rn concentrations are measured using an
atmospheric radon monitor (ARMON) (Grossi et al., 2012, 2016). A schematic
diagram of the measurement set-up used at the GIC3 station is shown in
Fig. S3.
The Picarro Inc. G2301 analyser is calibrated every 2 weeks using four
secondary working gas standards, which are calibrated at the beginning and
end of their lifetime against seven standards of the National Oceanic and
Atmospheric Administration (NOAA) (calibration scales are WMO-CO2-X2007
and WMO-CH4-X2004 for CO2 and CH4, respectively). A target gas
is analysed daily for 20 min in order to check the stability and quality of
the instrument calibration. For the length of the study, the instrument
repeatability for CH4 was 0.80 nmol mol-1, the long-term
reproducibility was 0.36 nmol mol-1 and the observe bias was
0.81 nmol mol-1. Previous values were calculated according to the
definitions of the World Meteorological Organization (WMO, 2009). The ARMON
instrument was installed at the GIC3 station in collaboration with the
Institute of Energetic Techniques of the Universitat Politècnica de
Catalunya (INTE-UPC). The ARMON is a self-designed instrument based on
α spectrometry of 218Po, collected electrostatically on a
passive implanted detector. The monitor has a minimum detectable activity of
150 mBq m-3 (Grossi et al., 2012). The performance of the ARMON was
previously tested against a widely used 222Rn progeny monitor and good
results were observed (Grossi et al., 2016).
The responses of both the ARMON and G2301 analysers are influenced by the air
sample humidity level. Water correction factors for both instruments are
empirically determined and corrected following Grossi et al. (2012) and
Rella (2010), respectively.
Sample air drying system
The instruments used at the GIC3 station require a total flow of
2.5 L min-1 of sample air dried to a water concentration lower than
1000 ppm to perform simultaneous measurements of GHG and 222Rn
concentrations. In the GIC3 inlet system, as shown in Fig. S3, the sample air
is passed through a Nafion® membrane
(Permapure, PD-100T-24MPS) that exchanges water molecules with a dry
counter-current air flow. The counter-current air flow is dried in a two-step
process, first through a cooling coil in a refrigerator at 3 ∘C and
a pressure of 5.5 barg, and then a cryotrap is used at -70 ∘C and
a pressure of 1.5 barg. Multiple cryotraps are selected with electrovalves
in order to increase the autonomy of the system to about 2 months. The
typical water content of sample air inside the instruments is between 100 and
200 ppm.
Meteorological observations
Meteorological variables are continuously measured at the GIC3 tower. The
canopy around the tower is below 20 m. The area surrounding the GIC3 station
is hilly as shown on the topographic map of Fig. S1. The tower is equipped
with (1) two-dimensional sonic anemometer (WindSonic, Gill Instruments) for
wind speed and direction (accuracies of ±2 % and ±3∘,
respectively); (2) humidity and temperature probe (HMP 110, Vaisala) with an
accuracy of ±1.7 % and ±0.2 ∘C, respectively;
(3) barometric pressure sensor (61302V, Young Company) with an accuracy of
0.2 hPa (at 25 ∘C) and 0.3 hPa (from -40 to
+60 ∘C). All the accuracies refer to the manufacturer's
specifications.
Planetary boundary layer height (PBLH)
Planetary boundary layer height (PBLH) data used in this analysis have been
extracted from the operational high-resolution atmospheric model of the
European Centre for Medium-Range Weather Forecasting (ECMWF-HRES) (ECMWF,
2006) for the period of interest (January 2013–December 2015) for the GIC3
area. This model stores output variables every 12 h (at 00:00 and
12:00 UTC) with a temporal resolution output of 1 h and with forecasts from
+00:00 to +11:00 h. The horizontal spatial resolution of the model is
about 16 km. In the ECMWF-HRES model the calculation of the PBLH is based on
the bulk Richardson number (Ri) (Troen and Mahrt, 1986). As regards the
reliability of modelled PBLH data, Seidel et al. (2012) have shown that data
limitations in vertical profiles introduce height uncertainties that can
exceed 50 % for shallow boundary layers (< 1 km), but are
generally < 20 % for deeper boundary layers. In addition, they
compared radiosonde observations with re-analysis and climate models and
showed that these latter two produce deeper layers due to the difficulty in
simulating stable conditions.
CH4 fluxes
CH4 fluxes based on FLEXPART footprints and the radon
tracer method
The RTM is a well-known method (e.g. Hammer and Levin, 2009) and it has been
used in this study, following Vogel et al. (2012), in order to obtain
observation-based estimates of nocturnal CH4 fluxes at GIC3. The RTM
uses atmospheric measurements of 222Rn and measured, or modelled, values
of 222Rn fluxes together with atmospheric mixing ratios of a gas of
interest, i.e. CH4, in order to retrieve the net fluxes of this gas
(e.g. Hammer and Levin, 2009; Grossi et al., 2014). This method is based on
the assumption that the nocturnal lower atmospheric boundary layer can be
described as a well-mixed box of air (Schmidt et al., 1996; Levin et al.,
2011; Vogel et al., 2012). The boundary layer is considered homogeneous
within the box and with a time-varying height. No significant horizontal
advection is considered due to stable atmospheric conditions (Griffiths et
al., 2012). In this atmospheric volume the variation of the concentration of
any tracer (shown with the subindex i) with time Ci(t) will be
proportional to the flux of the tracer Fi(t) and inversely
proportional to the height of the boundary layer h(t) (Eq. 1; e.g.
Galmarini, 2006; Griffiths et al., 2012; Vogel et al., 2012; Grossi et al.,
2014).
dCi(t)dt∝Fi(t)⋅1h(t)
Applying Eq. (1) for both 222Rn and CH4, Equation (2) is obtained,
with a dimensionless conversion factor c derived from the observed slope of
the concurrent concentration increase in both gases:
dCCH4tdtdC222Rntdt⋅F222Rn=c⋅F222Rn=FR_CH4,
observing the concentration increase in two gases that fulfil the above
assumptions, here CH4 and 222Rn. If the flux of 222Rn is
known, then the flux of CH4 can be calculated (Levin et al., 2011). A
description of the specific criteria used to implement the RTM can be found
in detail in Vogel et al. (2012). Grossi et al. (2014) previously applied the
RTM for the first time at the GIC3 station using only a 3-month dataset and
with a constant (in time and space) 222Rn flux of
60 Bq m-2 h-1. Here, in order to apply the RTM to retrieve a
time series of CH4 fluxes (FR_CH4) during 2013–2015 at the GIC3
station and to compare these results with those obtained using a bottom-up
inventory for methane (FE_CH4), we used the following extensive set-up:
A nocturnal window between 20:00 and 05:00 UTC was selected for each
single night analysis in order to utilise only accumulation events in which
atmospheric concentrations of both CH4 and 222Rn had a positive
concentration gradient due to positive net fluxes under stable boundary layer
conditions.
A data selection criterion based on a threshold of R2≥0.8 for the
linear correlation between 222Rn and CH4 was used to reject events
with low linear correlation between the atmospheric concentrations of both
gases.
An effective radon flux influencing the GIC3 station each night from
2013 to 2015 was calculated by coupling local radon flux data, obtained using
the output for the local pixel containing the GIC3 station of the model
developed by López-Coto et al. (2013), with the footprints calculated by
the ECMWF-FLEXPART model (version 9.02) (Stohl, 1998). Local radon flux data were
calculated as explained in the following paragraph, while the footprints
obtained are described in Sect. 2.4.3.
The radon flux model of the University of Huelva (from now on named the UHU model)
employed in this work has been described in detail by López-Coto et
al. (2013). By using this model, a time-dependent inventory was calculated
for the period 2011–2014 by employing several dynamic inputs, namely soil
moisture, soil temperature and snow cover thickness. These data were obtained
directly from Weather Research and Forecasting (WRF) simulations (Skamarock
et al., 2008). A domain of 97 × 97 grid cells centred on Spain with
a spatial resolution of 27 × 27 km2 and a temporal resolution
of 1 h was defined. The 222Rn flux data calculated using this model were
only available until November 2014 due to a lack of WRF simulations. In order
to obtain data for this period when modelled 222Rn flux data were not
available, from December 2014 to December 2015, a seasonal and monthly
climatology was calculated by using the UHU data set model for the years
2011–2014. Karstens et al. (2015) compared the 222Rn flux values
calculated over Europe by their model to UHU values and to long-term direct
measurements of 222Rn exhalation rates in different areas of Europe.
They found a generally 40 % higher 222Rn exhalation rate on their
map than estimated by the UHU map over Europe. This previous result has been
taken into consideration within the present study to better interpret the
obtained data.
CH4 fluxes based on FLEXPART footprints and the EDGARv4.2 inventory
grid map
Bottom-up CH4 fluxes influencing the GIC3 station were estimated by
using the footprints calculated by the ECMWF-FLEXPART model (obtained as
described in Sect. 2.4.3) and the Emissions Database for Global Atmospheric
Research (EDGAR) version 4.2 (EDGAR, 2010). The EDGAR inventory, developed by
the European Commission Joint Research Centre and the
Netherlands Environmental Assessment Agency, includes global
anthropogenic emissions of GHGs and air pollutants by country on a spatial
grid. The EDGAR version used in the present study provides global annual
CH4 emissions on a 0.1∘ (11 km) resolution for the year 2010.
All major anthropogenic source sectors, e.g. waste treatment, industrial and
agricultural sources (e.g. enteric fermentation) are included, whereas
natural sources (e.g. wetlands or rivers) are not. The spatial allocation of
emissions on 0.1∘ by 0.1∘ grid cells in EDGAR has been built
up by using spatial proxy datasets with the location of energy and
manufacturing facilities, road networks, shipping routes, human and animal
population density, and agricultural land use. UNFCCC reported national sector
totals are then removed with the given percentages of the spatial proxies
over the country's area (EDGAR, 2010). Figure 1 shows the EDGAR inventory
grid map extracted for Spain.
The influence of the emissions associated with the cities surrounding the
region of GIC3 was also modelled using this inventory to better understand
their impact. In Table S1 the coordinates of the upper and lower corners of
the areas used to describe the location of the metropolitan areas over the
EDGAR inventory are reported.
CH4 EDGARv4.2 inventory grid map extracted for Spain (year
2010).
Footprints
The Lagrangian particle dispersion model FLEXPARTv9.0.2 has been extensively
validated and is nowadays widely used by the scientific community to
calculate atmospheric source-receptor relationships for atmospheric gases and
organic particles (e.g. Stohl, 1998; Stohl et al., 2005; Arnold et al., 2010;
Font et al., 2013; Tohjima et al., 2014). FLEXPART allows computation of the
trajectories of virtual air parcels arriving at the receptor point, i.e. the
GIC3 station, at a specific time. FLEXPART has been applied here to calculate
24 h backward trajectories of 10 000 virtual air parcels starting at
00:00 UTC for each night of the period 2013–2015. Each back trajectory
simulation was run with a time-step output of 3 h. Meteorological data from
the operational ECMWF-HRES model with a resolution of 0.2∘ were used
as input fields for the FLEXPART modelling. The FLEXPART output domain
resolution was 0.2∘. The domain was set at 25∘ N,
40∘ W for the lowest left corner and 65∘ N, 10∘ W
for the upper right corner. A nested output domain of 0.05∘
resolution was defined at 37∘ N, 12∘ W for the lowest left
corner and 43∘ N, 0∘ E for the upper right corner. The
FLEXPART model accounts for both the vertical and horizontal position of the
virtual air parcels and their residence time in each grid cell. This
information allows the influence of atmosphere–surface exchange to be
estimated on the observed concentrations if air parcels are within the
boundary layer. A maximum height of 300 m a.g.l. has been selected for the
footprint analysis following Font et al. (2013). The average nocturnal
footprint for the period 2013–2015 is presented in Fig. 2. The footprints
obtained for the nested FLEXPART domain were combined with the EDGAR
inventory map for CH4 emissions (EDGAR, 2010) and with the UHU
222Rn flux inventory map (López-Coto et al., 2013), separately, in
order to obtain the time series of modelled CH4 and effective
222Rn fluxes. The resulting mean flux Fi(S,tn), for each gas
i, at the receptor S (GIC3 station) and for each night tn, with
n ranging over the 3-year period, is thus given by Eq. (3):
FiS,tn=∑t=tnot=tn∑xFix,tn⋅wx,T,
where t ranges between the 24 h of back-trajectories, Fi(x,t) denotes
the flux of a given grid cell x at time t derived from the EDGAR or UHU
inventory map, separately. The weighting factor of each grid cell w(x,T) is
calculated using the FLEXPART footprint for each night tn over the
3-year period and it has been calculated by normalising the residence time of
each grid cell over the nested domain and during the 24 h back-trajectories
(T), as given by Eq. (4):
∑x,tTw(x,t)=1.
Average nocturnal FLEXPART footprint for the 2013–2015 period
(residence time t is on the logarithmic scale).
Results
In this section we present the results of the daily and seasonal atmospheric
CH4 variability at GIC3 station analysed using a record of 3-year hourly
CH4 and 222Rn time series. Unfortunately, due to problems in the
air sample system, data for 11 % of the time period are not available,
mainly in the summer of 2013.
Grossi et al. (2016) presented a complete characterisation of the main
meteorological conditions and 222Rn behaviour at the ClimaDat stations
including GIC3, and we will use these previous results to interpret
atmospheric processes and the variability of CH4 mixing ratio, as well
as to understand the dominating wind regimes for CH4 flux data analysis
(Fig. S4 presents the monthly wind regimes observed at the GIC3 station both
for day-time and night-time).
Statistics of the daily and seasonal atmospheric CH4
variability
The 3-year hourly time series of atmospheric CH4 mixing ratios measured
at GIC3 shows a median value of 1904.5 nmol mol-1 with an absolute
deviation of 29.6 nmol mol-1. The box plots in Fig. 3 present the
medians of the atmospheric CH4 mixing ratios and 222Rn
concentrations measured at the GIC3 station over the dataset on an hourly
(left panels) and a monthly (right panels) basis. Monthly means have been
calculated separately for day-time (07:00–18:00 UTC) and night-time
(19:00–06:00 UTC).
Box plots of hourly (a, c) and monthly (b, d)
atmospheric CH4 mixing ratios (a, b) and 222Rn
concentrations (c, d) measured from January 2013 to December 2015 at
the GIC3 station. For each median (black bold line) the 25th (Q1; lower box
limit) and 75th (Q3; upper box limit) percentiles are reported in the plot.
The lower whisker goes from Q1 to the smallest non-outlier in the data set,
and the upper whisker goes from Q3 to the largest non-outlier. Outliers are
defined as > 1.5 IQR or < 1.5 IQR (IQR: interquartile range).
The maximum hourly median methane mixing ratio measured within the 3-year
observation period is 1921.1 nmol mol-1 and is observed at 03:00 UTC,
whereas the minimum hourly median value of 1889.9 nmol mol-1 is
observed at 13:00 UTC. The absolute standard deviation of the hourly median
is 16.97 nmol mol-1. The hourly median daily amplitude at this
station, between the minimum and the maximum, is 31.18 nmol mol-1.
CH4 concentrations usually start decreasing at GIC3 in the morning at
around 07:00 and 08:00 UTC and begin to increase again in the afternoon at
around 17:00 and 18:00 UTC. Night-time CH4 concentrations present an
absolute standard deviation of 60 nmol mol-1, while for day-time
concentrations it is 30 nmol mol-1. The same pattern is observed in
the daily cycle of atmospheric 222Rn (Grossi et al., 2016). Monthly
day-time and night-time medians of CH4 mixing ratios and 222Rn
concentrations show different patterns, as seen in Fig. 3b, d. The night-time
monthly medians of methane mixing ratio measured in the months between June
and December look higher than those measured between January and May.
Night-time monthly medians of measured 222Rn concentration are highest
between July and August.
Daily and seasonal PBLH variability
Figure 4 shows the hourly median (a) and the monthly median (b) variability
of the PBLH data extracted from the ECMWF-HRES model for the grid containing
the GIC3 station. On a daily basis the hourly median of the PBLH reaches its
minimum during night-time between 23:00 and 07:00 UTC. The PBLH starts to
increase at around 08:00 UTC, reaching its maximum between 14:00 and
16:00 UTC and then decreases again after 17:00 UTC. On a monthly basis, the
day-time monthly median PBLH reaches its minimum during the winter months of
January and December, while it reaches its maximum in the summer months. The
highest night-time monthly medians for the PBL heights are observed in
winter. The day-time monthly PBLH medians present a quite symmetric
distribution (around July as a centre line), similar to the night-time
monthly 222Rn medians (Fig. 3d).
Box plots of hourly (a) and monthly (b) PBLH data
extracted from the ECMWF-HRES model for the period
January 2013–December 2015 at the GIC3 station. For each median (black bold
line) the 25th (Q1; lower box limit) and 75th (Q3; upper box limit)
percentiles are reported in the plot. The lower whisker goes from Q1 to the
smallest non-outlier in the data set, and the upper whisker goes from Q3 to
the largest non-outlier. Outliers are defined as > 1.5 IQR or
< 1.5 IQR (IQR: interquartile range).
Comparison between CH4 and 222Rn variability
A comparison of the daily and seasonal variability of the atmospheric
concentrations of 222Rn and CH4 in relation to changes in height of
the PBL at the GIC3 station (2013–2015) is presented in Figs. 5 and 6,
respectively.
The daily evolution of hourly means of the 222Rn atmospheric
concentrations (Fig. 5a) implies that on a daily timescale, when 222Rn
flux can be considered fairly constant (e.g. López-Coto et al., 2013),
PBLH variations drive the increase or decrease in the atmospheric 222Rn
concentrations. In this sense, 222Rn seems to be an excellent predictor
of PBLH (and vice versa) on a daily timescale. Looking at the hourly means
of the atmospheric CH4 mixing ratios (Fig. 5b), we can observe that
methane decreases as the PBLH increases, as was observed for 222Rn.
However, between 12:00 and 18:00 UTC higher values in CH4 mixing ratios
relative to the values observed between 10:00 and 12:00 UTC are observed,
which have similar PBLH conditions and could indicate some daily variability
in the methane fluxes.
Relation between hourly means of atmospheric CH4
(b) and 222Rn (a) concentrations measured during
2013–2015 at the GIC3 station and ECMWF data of PBLH for the same area and
for the same time interval.
To interpret the monthly variability, the daily amplitude for each gas, i.e.
Δ222Rn for radon and ΔCH4 for methane, was calculated
in order to subtract the influence of the changing daily background
contribution measured at the GIC3 station. The term Δ222Rn is defined as the difference between average
night-time concentration data (19:00–06:00 UTC) versus average day-time
(07:00–18:00 UTC) concentration data (Eq. 5), and ΔCH4 has
been calculated accordingly.
Δ222Rn=222Rnnighttime-222Rnday-time
Figure 6 reveals that monthly amplitudes increase in summer, when the day-time
PBLH increases very strongly due to vertical mixing (see Fig. 4). This
general tendency is found both for 222Rn and CH4 concentrations.
Concentration amplitudes of 222Rn are higher in autumn than in winter
under the same PBLH conditions (Fig. 6a). This could indicate that some
process other than PBLH is driving this difference in the 222Rn
concentrations. In Fig. 7 we observe how the monthly 222Rn flux
calculated by the UHU model (presented in Sect. 2.4) changes.
In agreement with Grossi et al. (2016), we find a lower 222Rn flux at
GIC3 during winter due to snow cover events and low temperatures, which
prevent 222Rn diffusion from the soil. The 222Rn flux then
increases almost two-fold and three-fold during the autumn and summer months,
respectively. This is due to drier soil conditions and the high gradient of
temperature in the surface atmospheric layer which facilitates the escape of
222Rn from the pores of the granitic soil (Nazaroff and Nero, 1988).
This seasonality of the 222Rn flux could be the main cause of the
increased atmospheric Δ222Rn under the same PBLH conditions.
Monthly variations of ΔCH4 shown in Fig. 6 (bottom panel)
display no clear simple correlation with PBLH. The ΔCH4 appears to
be higher between the months of June and December irrespective of the
corresponding PBLH values.
Relation between monthly means of concentration amplitudes of
ΔCH4 (b) and Δ222Rn (a) measured
during 2013–2015 at the GIC3 station and monthly ECMWF data of PBLH for the
same area during same time interval.
Monthly 222Rn flux means calculated by the UHU model and
climatology for 2013–2015 at the GIC3 station. Coloured circles indicate the
same months as in Fig. 6.
Variations of CH4 fluxes
So far, daily variations for both CH4 mixing ratio and 222Rn
concentrations can be mainly explained in relation to the accumulation or
dilution of gas concentrations within the PBL. However, the hysteresis
observed for the CH4 mixing ratio of Fig. 5b seems to indicate a small
change in the methane source between 12:00 and 18:00 UTC.
Monthly Δ222Rn variability can be understood when we account
for seasonal 222Rn flux changes. Unfortunately, existing emission
inventories (EDGAR, 2010; MMA, 2016) generally do not yet provide
seasonally, hourly varying CH4 emission values either for Europe in
general or for Spain in particular.
In order to understand the impact that temporal changes of CH4 emissions
may have on monthly mean atmospheric CH4 mixing ratios, we have applied
two different methodologies, as explained in Sect. 2.4.1 and 2.4.2, and we
have compared their resulting fluxes: FR_CH4 and FE_CH4,
respectively. Figure 8 presents the effective 222Rn flux time
series used for the application of the first methodology (RTM), together with
the raw 222Rn flux calculated by the UHU model and its seasonal
climatology.
Time series of local 222Rn flux calculated by the UHU model
(black line; López-Coto et al., 2013) for the GIC3 area, 222Rn flux
seasonal climatology (blue line) and effective 222Rn flux
calculated on the basis of FLEXPART footprints (red dots). This last series
was used within the RTM.
Figure 9 presents the time series of CH4 fluxes estimated at the GIC3
station and Ti (grey shaded rectangles) indicates the time when
transhumant livestock returns to the GNP after spending the winter in the
south of Spain (Tapias, 2014; Rodríguez, 2015). The green shaded areas
indicate the periods, between June and December, when transhumant livestock
typically stays in the GIC3 region (Ruiz Perez and Valero Sáez, 1990;
López Sáez et al., 2009; Libro Blanco, 2013). Data coverage in the
second part of the time series (2014–2015) is higher than in the first
period (2013–2014) because the simultaneous availability of 222Rn and
CH4 data was higher. The mean of FR_CH4 fluxes over the dataset is
0.11 mg CH4 m-2 h-1 with 25th and 75th percentiles of 0.07
and 0.14 mg CH4 m-2 h-1, respectively. The mean of
FE_CH4 fluxes over the dataset is 0.33 mg CH4 m-2 h-1
with 25th and 75th percentiles of 0.28 mg CH4 m-2 h-1 and
0.36 mg CH4 m-2 h-1, respectively. FR_CH4 fluxes are
constantly lower than FE_CH4 fluxes, although this discrepancy
decreases during some periods, as we will investigate later. FEC_CH4
fluxes obtained with the EDGARv4.2 inventory by considering only the
contribution of the cities that are located around the GIC3 station, in
agreement with the masks presented in Table S1, had a total mean value over
the dataset of 0.02 mg CH4 m-2 h-1 with 25th and 75th
percentiles of 0 mg CH4 and 0.006 mg CH4 m-2 h-1,
respectively.
Results of night-time FR_CH4 fluxes (mg
CH4 m-2 h-1) (red circles) obtained at the GIC3 station from
January 2013 to December 2015 compared with night-time FE_CH4 fluxes
obtained using bottom-up inventory emissions (grey line), and calculated
FEC_CH4 fluxes from contributions from surrounding cities (green
circles). The weeks Ti represent the period of 2014 (21–27 June) and
2015 (20–26 June) when transhumant livestock returned to the GIC3 area after
spending the winter in the south of Spain and concurrent with the
availability of FR_CH4 fluxes data. Shaded green regions represent the
periods when transhumant livestock remain in the GIC3 area.
Figure 10 shows monthly box plots of FE_CH4 and FR_CH4 fluxes.
Shaded areas are coloured according to the main local wind directions
reaching the GIC3 station at night. This classification is based on the
results presented in Fig. S2, where monthly windrose plots for the GIC3
station between 2013 and 2015 are shown. We can observe that there is no
variability in monthly FE_CH4 flux values. In contrast, FR_CH4
flux results show an increase in CH4 fluxes between June and December
that seems to be independent of the seasonally changing dominant wind
directions. This increase is also uncorrelated with seasonally changing
222Rn fluxes (Fig. 7). The seasonal change of CH4 fluxes between
the first and the second half of the year at GIC3 could indeed be related to
variations in local CH4 emissions. The period between June and December
represents the time of year when transhumant livestock returns to the GNP.
The contribution of cities is only visible during certain months and it seems
to be related with winds coming from the east in the direction of the Madrid
urban area (see Fig. S2).
Box plots of monthly CH4 fluxes (mg
CH4 m-2 h-1) calculated for the GIC3 area using the RTM
(red) and the EDGAR inventory (total in yellow; contribution of
cities in green). Coloured areas indicate main wind directions for specific
months. For each median (black bold line) the 25th (Q1; lower box limit) and
75th (Q3; upper box limit) percentiles are reported in the plot. The lower
whisker goes from Q1 to the smallest non-outlier in the data set, and the
upper whisker goes from Q3 to the largest non-outlier. Outliers are defined
as > 1.5 IQR or < 1.5 IQR (IQR: interquartile range).
The disagreement observed between FE_CH4 and FR_CH4 fluxes in the
months between June and December (Figs. 9 and 10), when the transhumant
livestock is in the GIC3 area, may be due to different reasons:
A possible underestimation of the 222Rn flux outputs from the UHU radon
flux model could occur, which would lead to lower FR_CH4 fluxes (Eq. 2). As
explained previously, Karstens et al. (2015) compared their radon flux model
with the UHU model and it gave, generally, 40 % higher 222Rn flux
values than the UHU model over Europe.
The methodology used within the
EDGAR for the spatial disaggregation of national sector emission over the
country could lead to a distribution of CH4 emission in the GIC3 region
higher than true levels leading to an overestimation of the FE_CH4.
The fixed height of 300 m used for the calculation of nocturnal
footprints could introduce a bias. However, this value is well within the
range of nocturnal PBLH values calculated with data extracted from the
ECMWF-HRES model. Furthermore, the calculated FLEXPART footprints were used
both for FR_CH4 and FE_CH4 calculations and this should not
affect the relative differences between their values.
When applying a 40 % increase for the local 222Rn source, as
suggested by Karstens et al. (2015), we can re-calculate FR_CH4
emissions as FR_CH4_rescale. The box plot of the monthly medians of
FE_CH4, FR_CH4 and FR_CH4_rescale are compared in Fig. 11.
The mean of FR_CH4_rescale fluxes over the dataset is 0.29 mg
CH4 m-2 h-1 with 25th and 75th percentiles of 0.17 mg
CH4 and 0.34 mg CH4 m-2 h-1, respectively.
FR_CH4_rescale is in agreement with FE_CH4 fluxes during the
months between June and December, when the transhumant livestock remains in
the GIC3 area (cattle season).
Box plots of monthly CH4 fluxes (mg
CH4 m-2 h-1) calculated for the GIC3 area using the RTM
(red), the EDGAR inventory (yellow) and RTM using the
222Rn flux comparison factor found by Karstens et al. (2015) (grey).
Coloured areas indicate main wind directions for specific months. For each
median (black bold line) the 25th (Q1; lower box limit) and 75th (Q3; upper
box limit) percentiles are reported in the plot. The lower whisker goes from
Q1 to the smallest non-outlier in the data set, and the upper whisker goes
from Q3 to the largest non-outlier. Outliers are defined as > 1.5
IQR or < 1.5 IQR (IQR: interquartile range).
To highlight seasonal differences, FE_CH4, FR_CH4 and
FR_CH4_rescale fluxes are aggregated into two box plots in Fig. 12,
according to the no-cattle season (January until May), when there is no
livestock in the GIC3 area, and cattle season (June until December).
According to these data during the no-cattle season, FR_CH4 fluxes
present a mean value of 0.09 CH4 m-2 h-1 with a standard
deviation of 0.15 mg CH4 m-2 h-1. During the cattle season,
the mean value of FR_CH4 fluxes is 0.12 CH4 m-2 h-1
with a standard deviation of 0.05 mg CH4 m-2 h-1. The mean
value of FR_CH4_rescale fluxes is 0.24 mg
CH4 m-2 h-1 during the no-cattle season with a standard
deviation of 0.39 mg CH4 m-2 h-1 and it is 0.30 mg
CH4 m-2 h-1 during the cattle season with a standard
deviation of 0.12 mg CH4 m-2 h-1. The corresponding values
for FE_CH4 fluxes are 0.31 mg CH4 m-2 h-1 for the
no-cattle season and 0.32 mg CH4 m-2 h-1 for the cattle
season.
Box plots of FE_CH4, FR_CH4 and FR_CH4_corr fluxes
(in mg m-2 h-1) calculated for theGIC3 area during the “warm”
season (June–December, yellow box) and the “cold” season (January–May,
grey box). For each median (black bold line) the 25th (Q1; lower box limit)
and 75th (Q3; upper box limit) percentiles are reported in the plot. The
lower whisker goes from Q1 to the smallest non-outlier in the data set, and
the upper whisker goes from Q3 to the largest non-outlier. Outliers are
defined as > 1.5 IQR or < 1.5 IQR (IQR: interquartile range).
Discussion
The present results show the different influences that meteorological
conditions (PBLH and wind direction) and regional sources may have on the
variability of atmospheric CH4 concentrations observed at the GIC3
station. The 222Rn observations have been used, together with modelled PBLH
data, to better understand the reasons for the variability of the
atmospheric CH4 concentrations observed at the station for different
times scales. The use of 222Rn as a tracer to calculate independent
fluxes of GHGs has been shown in order to help with the improvement of
emission inventories on a regional scale.
Daily variability of atmospheric CH4 concentrations
The daily cycle of atmospheric CH4 mixing ratios (Fig. 3a) measured at
GIC3 shows significant changes between day-time and night-time periods. The
large increase in nocturnal CH4 mixing ratios can mainly be explained by
the decreased height of the planetary boundary layer (Fig. 4a), which is
supported by a similar behaviour of 222Rn concentrations (Fig. 3c).
Indeed, CH4, as well as 222Rn, reaches its maximum concentration
when the PBLH is below 300 m a.g.l. during the night, while their
atmospheric concentrations decrease with the increase in the PBLH during
day-time.
The correlation of PBLH and 222Rn (and CH4) in Fig. 5 indicates
that 222Rn fluxes do not strongly vary on daily timescales or, at
least, not to a degree that can influence their atmospheric concentration
variability. CH4 fluxes seem to change on a daily timescale. Average
afternoon CH4 concentrations are slightly enhanced compared to those
from the morning for similar PBLH values (Fig. 5b). They show a hysteresis
behaviour which could indicate local emissions increase or that a systematic
transport of CH4 enhanced air masses, not rich in radon, occurs at GIC3.
Some studies (e.g. Bilek et al., 2001; Wang et al., 2015) have found strong
emission increases from dairy cows after feeding in feedlots, while McGinn et
al. (2010) only found small diurnal increases in CH4 emissions between
11 and 17 h for grazing cattle. Unfortunately, no detailed information about
the feeding cycle of the GIC3 livestock is available, but grazing should be
considered the predominant form of livestock management in transhumance. However, Figs. 9 and 10 together with Fig. S4 show the influences of
eastern winds, coming from the Madrid direction, on the CH4 fluxes.
Seasonal variability of atmospheric CH4 concentrations
To understand the drivers of monthly changing concentrations of CH4, we
need to account for PBLH local meteorology, changing regional emissions and
changing background concentrations of CH4 at GIC3. Median monthly mixing
ratios for day-time and night-time (Fig. 3b) are discussed alongside ΔCH4 (Fig. 6), which allows us to subtract seasonal and synoptic
background variations. This enables us to focus on the impact of PBLH for
individual days that are then averaged to investigate how ΔCH4
changes on a monthly basis. The observed variability of ΔCH4
(Figs. 3b and 6) cannot be explained only in terms of changes of the PBLH.
Monthly averages of ΔCH4 (and night-time monthly CH4
box plots, Fig. 3b) present their maximum values between June and December,
and their minimum values during the rest of the months irrespective of the
height of the PBL. From co-located 222Rn concentration observations we
learn that an increase in the average monthly fluxes (Fig. 7) can compensate
the effect of increased dilution in the deeper summer PBL on the observed
concentrations (Fig. 6a), yielding similar atmospheric 222Rn
concentrations. The increase in the modelled 222Rn flux in the GIC3
region from the winter to autumn season and the following decrease can
coherently help to explain the variation observed in monthly
Δ222Rn. Thus, the comparison between ΔCH4 and
Δ222Rn suggests that there may also be a monthly variability in the
sources of CH4 which should help to understand monthly atmospheric
mixing ratios variability. This has been further confirmed by our
FR_CH4 flux estimates, as seen in Figs. 9, 10 and 11. Of course, the
FR_CH4 flux estimates are limited to night-time due to the RTM
hypothesis. FR_CH4 fluxes show a total mean value 33 % lower than
FE_CH4 fluxes over the data set. When 222Rn fluxes are rescaled
according to Karstens et al. (2015), this difference is drastically reduced
to 10–15 %.
RTM-based CH4 fluxes show an increase of 25 % during the second
semester of the year on a monthly basis. This increase coincides with the
period of the year when transhumant livestock resides in the GIC3 region.
Although no exact information is available on the number of animals present
only in the GIC3 area, during this period of enhanced ruminant emissions, the
difference between CH4 fluxes based on RTM and the EDGAR inventory is
reduced from 73 to 65 % for FR_CH4 and from 27 to 9 % for
FR_CH4_rescale The difference during the no-cattle season is likely
due to the constant annual emission factor of CH4 emission used within
the bottom-up inventory which, of course, cannot yet reflect transhumance
activity. The likely explanation is that all emissions from the
aforementioned animals has been constantly allocated to this region, which is
why FE_CH4 is also larger than FR_CH4_rescale during months when
they are not present. The RTM analysis performed here suggests that
transhumance could be a relevant process to the understanding of sub-annual CH4
emissions in the region and can affect the spatial distribution of CH4
sources within a country. Our study indicates that the choice of 222Rn
model has an important impact on annual total emissions calculated, while
seasonal and short-term patterns are preserved.
Conclusions and outlook
To gain a full picture of the Spanish (and European) GHG balance,
understanding of CH4 emissions in different regions is a critical
challenge, as is the improvement of bottom-up inventories for all
European regions. Our study uses, among other elements, GHG, meteorological and
222Rn tracer data from one of the eight stations of the new ClimaDat
network in Spain, which provides continuous atmospheric observations of
CH4 and 222Rn in a region of Europe. The present study underlines
the fact that this data, combined with retrieved PBLH data and atmospheric
transport modelling (FLEXPARTv92) can help to understand the main causes of
temporal variability of GHG mixing ratios and can offer new insights into
regional emissions by identifying the impacts of changing sources, e.g.
emissions from transient livestock.
These first promising results should lead to further application of this RTM
to other GHG time series from the ClimaDat network and potentially in
continent-wide networks such as ICOS that routinely perform co-located GHG
and 222Rn observations. Particularly, the usefulness of the RTM has been shown, while also highlighting the need to improve this
method, especially in regard to (i) validation of the 222Rn flux maps
applied within the RTM and (ii) standardization of the footprint calculation.
Although the transhumant livestock seems to be the likely reason for the
seasonal changes observed in the FR_CH4 fluxes at the GIC3 station,
other sources could also contribute to this seasonality, such as waterbodies
or other natural emissions. These previous sources are not included in the
EDGAR inventory, but they could be detected by the RTM. However, those
sources would not be able to fully explain the sudden onset of increased
RTM-based CH4 fluxes but would rather contribute to a slow increase in
warmer months. Further research applying isotopic analysis of CH4 mixing
ratios measured at the GIC3 station for the different seasons should be
carried out, as well as transects of the regions to assess the impact of
natural sources on CH4 mixing ratios. In addition, no precise data on
transhumant activity in Spain is available to date, but our study suggests
the existence of a link between regional CH4 fluxes and highlights the
need for more information on transhumance activity which could be taken into
account in future emission inventories of this region (and Europe). In
addition, our results show that urban emissions can be transported and could
influence the atmospheric composition of remote rural areas over several
hundred kilometres under specific synoptic conditions.