GMDGeoscientific Model DevelopmentGMDGeosci. Model Dev.1991-9603Copernicus PublicationsGöttingen, Germany10.5194/gmd-9-3137-2016Evaluation of the boundary layer dynamics of the TM5 model over EuropeKoffiE. N.ernest.koffi@jrc.ec.europa.euhttps://orcid.org/0000-0002-7692-4328BergamaschiP.https://orcid.org/0000-0003-4555-1829KarstensU.https://orcid.org/0000-0002-8985-7742KrolM.SegersA.SchmidtM.LevinI.VermeulenA. T.https://orcid.org/0000-0002-8158-8787FisherR. E.KazanV.Klein BaltinkH.LowryD.https://orcid.org/0000-0002-8535-0346MancaG.MeijerH. A. J.MoncrieffJ.PalS.RamonetM.ScheerenH. A.WilliamsA. G.https://orcid.org/0000-0002-0568-8487European Commission Joint Research Centre, Ispra (Va), ItalyMax-Planck-Institute for Biogeochemistry, Jena, GermanyICOS Carbon Portal, ICOS ERIC at Lund University, Lund, SwedenSRON Netherlands Institute for Space Research, Utrecht, the NetherlandsInstitute for Marine and Atmospheric Research Utrecht, Utrecht University, Utrecht, the NetherlandsMAQ, Wageningen University and Research Centre, Wageningen, the NetherlandsNetherlands Organisation for Applied Scientific Research (TNO), Utrecht, the NetherlandsInstitut für Umweltphysik, Heidelberg University, Heidelberg, GermanyEnergy research Center Netherlands (ECN), Petten, the NetherlandsRoyal Holloway, University of London (RHUL), Egham, UKLaboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, FranceRoyal Netherlands Meteorological Institute (KNMI), De Bilt, the NetherlandsCentrum voor Isotopen Onderzoek (CIO), Rijksuniversiteit Groningen, Groningen, the NetherlandsAtmospheric Chemistry Research Group, University of Bristol, Bristol, UKDepartment of Meteorology, Pennsylvania State University, State College, PA, USAAustralian Nuclear Science and Technology Organisation (ANSTO) Environment Research Theme, Locked Bag 2001, Kirrawee DC, NSW 2232, AustraliaE. N. Koffi (ernest.koffi@jrc.ec.europa.eu)14September2016993137316029February201630March201627July201628July2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://gmd.copernicus.org/articles/9/3137/2016/gmd-9-3137-2016.htmlThe full text article is available as a PDF file from https://gmd.copernicus.org/articles/9/3137/2016/gmd-9-3137-2016.pdf
We evaluate the capability of the global atmospheric transport model TM5 to
simulate the boundary layer dynamics and associated variability of trace
gases close to the surface, using radon (222Rn). Focusing on the
European scale, we compare the boundary layer height (BLH) in the TM5 model
with observations from the National Oceanic and Atmospheric Admnistration
(NOAA) Integrated Global
Radiosonde Archive (IGRA) and also with ceilometer and
lidar (light detection and ranging) BLH
retrievals at two stations. Furthermore, we compare TM5 simulations of
222Rn activity concentrations, using a novel, process-based 222Rn
flux map over Europe (Karstens et al., 2015), with harmonised 222Rn
measurements at 10 stations.
The TM5 model reproduces relatively well the daytime BLH (within 10–20 %
for most of the stations), except for coastal sites, for which differences
are usually larger due to model representation errors. During night,
however, TM5 overestimates the shallow nocturnal BLHs, especially for the
very low observed BLHs (< 100 m) during summer.
The 222Rn activity concentration simulations based on the new
222Rn flux map show significant improvements especially regarding the
average seasonal variability, compared to simulations using constant
222Rn fluxes. Nevertheless, the (relative) differences between
simulated and observed daytime minimum 222Rn activity concentrations
are larger for several stations (on the order of 50 %) than the (relative)
differences between simulated and observed BLH at noon. Although the
nocturnal BLH is often higher in the model than observed, simulated
222Rn nighttime maxima are actually larger at several continental
stations. This counterintuitive behaviour points to potential deficiencies
of TM5 to correctly simulate the vertical gradients within the nocturnal
boundary layer, limitations of the 222Rn flux map, or issues related to
the definition of the nocturnal BLH.
At several stations the simulated decrease of 222Rn activity
concentrations in the morning is faster than observed. In addition, simulated
vertical 222Rn activity concentration gradients at Cabauw decrease
faster than observations during the morning transition period, and are in
general lower than observed gradients during daytime. Although these effects
may be partially due to the slow response time of the radon detectors, they
clearly point to too fast vertical mixing in the TM5 boundary layer during
daytime. Furthermore, the capability of the TM5 model to simulate the diurnal
BLH cycle is limited by the current coarse temporal resolution (3 h/6 h) of
the TM5 input meteorology.
Introduction
The boundary layer, being the lowest portion of the atmosphere, is largely
affected by the Earth's surface forcing. This layer is usually separated from
the free troposphere (where the surface effects are weak) by a thin and
strongly stable layer (capping inversion) that traps turbulence, moisture,
and trace gases below. The thickness of the boundary layer is variable in
space and time and can range from tens of metres to 4 km, depending on both
the synoptic and local meteorological conditions (Stull, 1988). The height of
the boundary layer is a critical parameter in atmospheric transport models,
since it controls the extent of the vertical mixing of trace gases emitted
near the surface. Previous studies that evaluated the ability of atmospheric
transport models to reproduce boundary layer dynamics demonstrated the
importance of temporal resolution of meteorological data, horizontal and
vertical model resolutions, and parameterisations of vertical mixing (e.g.
Denning et al., 1999; Dentener et al., 1999; Krol et al., 2005; Locatelli et
al., 2015). The realistic simulation of boundary layer height (BLH) is
crucial, especially for regional flux inversions, which make use of networks
of surface and tower-based trace gas concentration measurements to capture
the signals of regional sources (and sinks). Regional inversions of
greenhouse gases (GHG) (CO2, CH4, N2O, halocarbons) were
reported especially for Europe and North America, making use of the
increasing number of regional monitoring stations in these areas (e.g.
Gerbig et al., 2003; Carouge et al., 2010; Bergamaschi et al., 2010; Corazza
et al., 2011; Manning et al., 2011; Broquet et al., 2013; Bergamaschi et al.,
2015; Ganesan et al., 2015) as well as aircraft observations (e.g. Kort et
al., 2008; Miller et al., 2013).
In order to evaluate the quality of such flux inversions, a thorough
validation of the applied atmospheric transport model is essential. In this
study, we present a detailed evaluation of the boundary layer dynamics of the
TM5 model (Krol et al., 2005), which is the global transport model used in
the TM5-4DVAR
inverse modelling system (Meirink et al., 2008), applied in several of the
European inversions mentioned above (Corazza et al., 2011; Bergamaschi et
al., 2010, 2015). As a first step, we compare the model BLH with the
sounding-derived BLH of the National Oceanic and Atmospheric Admnistration
(NOAA) Integrated Global
Radiosonde Archive (IGRA) (Seidel et al., 2012) at European scale. Radiosonde
data have been considered to give the most accurate BLHs (Collaud Coen et
al., 2014). The model BLHs are also compared to those derived from the
ceilometer and lidar (light detection and
ranging) measurements at two European stations (Cabauw and Traînou). As
a second step, we compare TM5 simulations of 222Rn activity
concentrations with measurements at 10 European stations. 222Rn is an
excellent tracer for boundary layer mixing due to its short lifetime
(half-life) of 3.82 days and has been widely used for model validation (e.g.
Jacob and Prather, 1990; Jacob et al., 1997; Dentener et al., 1999;
Chevillard et al., 2002; Taguchi et al., 2011) and mixing studies (e.g. see
reviews in Zahorowski et al., 2004; Chambers et al., 2011; Williams et al.,
2011, 2013). However, the use of 222Rn for this purpose has been limited
by the simplified assumption of constant 222Rn fluxes over land used in
most 222Rn validation studies published so far. It has also been limited
by the fact that the observed 222Rn activity concentrations from
different stations were not harmonised.
Here, we make use of a novel detailed 222Rn flux map over Europe
(Karstens et al., 2015) based on a parameterisation of 222Rn production
and transport in the soil as well as improved observed 222Rn activity
concentrations obtained through a detailed comparison study (Schmithüsen
et al., 2016). The development of this 222Rn flux map has been performed
within the European project InGOS (Integrated non-CO2 Greenhouse gas
Observing System), including also a comparison of different transport models
(including TM5). While this model comparison will be published elsewhere
(Karstens et al., 2014), we present here the analysis for the TM5 model
aiming at the identification and quantification of potential systematic
errors in the simulation of the BLH dynamics, which could directly translate
into systematic errors in the derived surface fluxes. Our study also includes
the evaluation of a new parameterisation of convection in TM5, based on
European Centre for Medium-Range Weather Forecasts (ECMWF) (re)analysis,
compared to the default convection scheme used so far, based on the
parameterisation of Tiedtke (1989).
ObservationsBoundary layer height
Vertical mixing in the atmospheric boundary layer is mostly turbulent. The
BLH is confined by a thin layer where steep vertical gradients of
meteorological variables, trace gases, and aerosols occur. Consequently, all
the observational devices built for the retrieval of BLH are based on the
search of the height at which the strongest gradients occur. These gradients
can be based either on the atmospheric potential temperature profile, the
wind profile, or the aerosol backscatter profile. For meteorological data
sets and atmospheric transport models, the bulk Richardson number
(Rib), a dimensionless parameter defined as the ratio of
turbulence due to buoyancy and the mechanic generation of turbulence by wind
shear, has been widely used to determine BLHs (e.g. Vogelezang and Holtslag,
1996; Seibert et al., 2000; Seidel et al., 2012).
Thus, the BLH is the vertical level at which the bulk Richardson number
reaches a critical value (Ric) characterising the passage of
turbulent flow to a laminar one. The general expression of Vogelezang and
Holtslag (1996) used to compute Rib is given as follows:
Rib=gθvsθvh-θvszh-zsuh-us2+vh-vs2+bu∗2,
where g is the gravitational acceleration (9.81 m s-2),
θv the virtual potential temperature, z the geopotential
height, u the zonal wind speed, and v the meridional wind speed. The
indices h and s denote the vertical layer, and the surface, respectively.
bu∗2 depicts the turbulence production due to the surface
friction, a term which also prevents an undetermined Rib in case
of uniform high wind speeds relevant for neutral boundary layers. b is a
coefficient estimated to be 100 (Vogelezang and Holtslag, 1996) and u∗ is the surface friction velocity. The geopotential height z is expressed
in metres. The virtual potential temperature θv is in
Kelvin, and the velocities are in m s-1.
The vertical profile of Rib is linearly interpolated between
consecutive vertical layers. The BLH is defined as the height, where
Rib reaches the Ric. Commonly, a Ric
value of 0.25 has been used (e.g. Vogelezang and Holtslag, 1996; Seibert et
al., 2000; Seidel et al., 2012). The boundary layer height is defined with
reference to surface elevation, and not to sea level (Seidel et al., 2012).
IGRA data
We use BLHs from the NOAA IGRA database, which covers the 1990–2010 period
(Seidel et al., 2012). The IGRA data are based on radiosonde measurements
that are usually released at 00:00 and 12:00 UTC. The IGRA radiosonde
network over Europe is shown in Fig. 1. The dynamic (wind speed and
direction) and thermal (temperature and humidity) profiles from the
radiosondes are utilised to compute BLHs using the bulk Richardson number
method (Eq. 1; Sect. 2.1). In these BLH calculations both the surface wind
(i.e. us and vs in Eq. 1) and the surface friction
velocity (u∗) are unknown and set to zero. The Ric is
set to 0.25 (instead of 0.3 as used in TM5; see Sect. 3.2). Further details
on the choice of the settings as well as the vertical profiles of the
dynamic, thermodynamic, and bulk Richardson number quantities are described
in Seidel et al. (2012). These settings for the IGRA database were also
adopted in the InGOS protocol for the evaluation of the transport models
involved in InGOS inverse modelling analyses (Karstens et al., 2014). The
methodological uncertainties in the IGRA BLH data were evaluated based on
paired soundings released at the same site (Seidel et al., 2012). Results
show that the choice of Ric does not introduce large uncertainty,
but other methodological choices (including surface wind-speed estimates and
vertical interpolation of the bulk Richardson number profile) as well as the
vertical resolution of the sounding data are larger sources of uncertainty in
the derived BLHs (Seidel et al., 2012). The authors reported relative
uncertainties in the IGRA BLHs that can be large (> 50 %) for
shallow BLHs (< 1 km; mainly observed during night or early in the
morning), but much smaller (usually < 20 %) for deep BLHs
(> 1 km) during daytime.
Observational network of InGOS greenhouse gas (CH4, N2O)
and radon (222Rn) concentration measurements and boundary layer height
observations, blue diamonds: INGOS stations that measure CH4 and/or
N2O concentrations; red circles: InGOS stations that measure radon
(222Rn) activity concentrations; black dots: all existing IGRA stations;
red dots: IGRA station closest to InGOS station; triangles: ceilometer/lidar
measurement sites (i.e. Cabauw/Traînou). The acronyms for the stations
measuring 222Rn activity concentrations are compiled in Table 1.
Lidar and ceilometer data
The principle of lidar is based on a pulsed laser light emitted into the
atmosphere, which is back-scattered by aerosol particles and molecules. The
lidar algorithms derive the BLHs by searching the location of the strongest
aerosol gradient in the vertical dimension (e.g. Haeffelin et al., 2012; Pal
et al., 2012; Griffiths et al., 2013; Pal et al., 2015). A ceilometer is a
“low-cost lidar”, which was initially used for the detection of cloud base
heights. However, since the backscatter signal of aerosols is lower than that
of clouds, the sensitivity of ceilometers in retrieving the boundary layer
height is much less than that of lidar instruments (Pal, 2014). In contrast
to IGRA data (i.e. radiosonde-based BLH), the ceilometer and lidar allow for
measurements of the diurnal BLH cycle. However, the algorithms of both lidar
and ceilometer have some difficulties to assign the BLH during night and tend
to wrongly attribute the height of the residual layer of aerosol (often with
larger signal) as the height of the real mixed layer (e.g. Angevine et al.,
1998; Eresmaa et al., 2006; Haij et al., 2006). Lidar/ceilometer nocturnal
BLHs are also higher due to the fact that their overlap height can be above
the nocturnal shallow BLH (Pal et al., 2015). Uncertainties in lidar
retrieved BLHs were assessed based on a comparison between radiosonde-based
BLHs and wavelet derived BLH estimates from lidar and found to be about 60 m
(Pal et al., 2013).
We use the BLHs retrieved from lidar and ceilometer measurements at Traînou
and Cabauw, respectively (see Fig. 1 for their locations). The lidar
(ALS-300) measurements at Traînou are described by Pal et al. (2012). The
ceilometer at Cabauw is part of the network of the Vaisala LD-40 ceilometer
in the Netherlands operated by the Royal Netherlands Meteorological
Institute (KNMI; Haij et al., 2006). We analyse the ceilometer measurements
at Cabauw for 2010 and the lidar data at Traînou for 2011. For Cabauw we
compare the ceilometer-based BLH for 2010 with the BLH data from the closest
IGRA station (De Bilt), with results shown in the Supplement (Fig. S1).
Description of the different surface stations measuring 222Rn
activity concentrations. The locations of the stations are shown in Fig. 1.
CB1 and CB4 are the 20 and 200 m levels of the Cabauw tower, respectively.
Altitude is the sampling altitude above sea level and height is the sampling
height above the surface.
Station IDNameCountryLatitudeLongitudeAltitude (a.s.l.)/height222RnReference(∘)(∘)above surface (m)instrumentPALPallasFinland67.9724.12572/7one-filter methodHatakka et al. (2003)TTAAngusUK56.55-2.98363/50ANSTObSmallman et al. (2014)LUTLutjewadthe Netherlands53.406.3561/60ANSTObvan der Laan et al. (2010)MHDMace HeadIreland53.33-9.9040/15one-filter methodBiraud et al. (2000)CBW (CB1)Cabauwthe Netherlands51.974.9319/20one-filter methodVermeulen et al. (2011)CBW (CB4)Cabauwthe Netherlands51.974.93199/200ANSTObVermeulen et al. (2011)EGHEghamUK51.43-0.5645/10one filter methodLevin et al. (2002)GIFGif-sur-YvetteFrance48.712.15167/7one-filter methodLopez et al. (2012),Yver et al. (2009)HEIHeidelbergGermany49.428.71146/30one-filter methodLevin et al. (2002)TRN (TR4)TraînouFrance47.952.11311/180ANSTObSchmidt et al. (2014)IPRIspraItaly45.808.63223/3.5 (15)aANSTObScheeren and Bergamaschi(2012)
a Measurements at 3.5 m
“normalised” to sampling height of 15 m based on wind-speed-dependent
correction (see Sect. 2.2). b Australian Nuclear Science and
Technology Organisation two-filter instrument.
Observed 222Rn activity concentrations
The observed 222Rn activity concentrations are obtained from two different measurement methods:
The “two-filter” method developed by the Australian Nuclear Science and
Technology Organisation (ANSTO) (Whittlestone and Zahorowski, 1998; Chambers
et al., 2011). After drawing the sampled air continuously through a delay
volume to let all short-lived 220Rn (thoron) gas in the sampled air
decay, it passes through a first filter that removes all ambient 222Rn
and 220Rn decay products. Filtered air then enters in a delay chamber in
which new 222Rn progeny (218Po and 214Po) are produced. An
internal flow loop within the delay chamber passes the air through a second
filter, which collects the new 222Rn progeny formed under controlled
conditions. Hence, in the ANSTO system 222Rn activity concentration in
the sampled air is measured directly through its newly formed progeny within
the controlled environment of the delay chamber (Whittlestone and Zahorowski,
1998; Zahorowski et al., 2004; Chambers et al., 2011). In routine operation,
ANSTO monitors are calibrated monthly by injecting 222Rn from a well
characterised (to about ±4 %) 226Radium source. For ambient air
measurements at 1 Bq m-3 activity concentration, the total uncertainty
of hourly measurements is of the order of 10 %, which includes uncertainty in
flow rate as well as counting statistics. The ANSTO two-filter detectors have a
response time of around 45 min, and are quite bulky (∼ 3 m2),
which can hinder their deployment in constricted locations.
The one-filter methods used at the European stations are all based on
the direct collection and counting of the short-lived ambient 222Rn and
220Rn (212Pb) decay products that are attached to aerosols in the
sampled air. These decay products are accumulated on either static or moving
aerosol filters and measured by α or β spectroscopy (see
references given in Table 1). In order to derive the atmospheric 222Rn
activity concentration, this method requires corrections for the atmospheric
radioactive disequilibrium between the measured 222Rn daughters
(214Po and/or 218Po) and 222Rn (e.g. Levin et al., 2002).
We use 222Rn activity concentration measurements from 10 European
stations over the 2006–2011 period (Fig. 1 and Table 1). The data from the
different stations have been harmonised based on an extensive comparison
study performed within the InGOS project (Schmithüsen et al., 2016).
Based on the tall tower measurements at Cabauw and Lutjewad conducted at
different heights above ground level as well as on an earlier comparison at
Schauinsland station (Xia et al., 2010) and new comparison measurements in
Heidelberg with an ANSTO system, correction factors for disequilibrium have
also been estimated (Schmithüsen et al., 2016). All data used in the
present study have been corrected accordingly and brought to a common ANSTO
scale. A typical uncertainty of 222Rn data from the different one-filter
systems, including the uncertainty of the disequilibrium, is estimated to
10–15 %.
At the monitoring station Ispra, 222Rn activity concentration has been
measured using an ANSTO instrument, sampling air at an inlet positioned at
3.5 m above the ground, close to the GHG-sampling mast with a height of
15 m. Recent additional 222Rn measurements using the 15 m inlet of the
GHG mast (employing an Alphaguard PQ2000 (Genitron) instrument, calibrated
against the ANSTO monitor) revealed significant differences of the 222Rn
activity at the two sampling heights during periods with low wind speeds.
These differences showed that there are significant vertical 222Rn
gradients close to the ground. Based on the comparison of the two sampling
heights during a 3-month period, we derive a wind-speed-dependent correction,
in order to “normalise” the entire time series of the ANSTO measurements
(at 3.5 m above ground) to the 15 m inlet, which is considered to be more
representative. The uncertainty of this wind-speed-dependent correction
(based on the 1 standard deviation during the 3-month comparison) is included in the time
series shown in the Supplement (Fig. S24).
Model simulationsTM5 model
TM5 is a global chemistry transport model, which allows two-way nested
zooming (Krol et al., 2005). In this study we apply the zooming with
1∘× 1∘ resolution over Europe, while the global domain is
simulated at a horizontal resolution of 6∘ (longitude) × 4∘ (latitude). TM5 is an offline transport model, driven by
meteorological fields from the ECMWF Integrated Forecast System (IFS) ERA-Interim reanalysis
(Dee et al., 2011). The spatial resolution of this data set is approximately
80 km (T255 spectral) on 60 vertical levels from the surface up to 0.1 hPa.
We employ the standard TM5 version with 25 vertical levels, defined as a
subset of the 60 layers of the ERA-Interim reanalysis. The extraction of the
meteorological fields is performed through a pre-processing software, which
supplies fully consistent meteorology data with those of ECMWF at the
different spatial resolutions of TM5 (Krol et al., 2005). The boundary
layer, the free troposphere, and the stratosphere are represented by 5 (up
to 1 km), 10, and 10 layers, respectively. The temporal resolution of the
data is 3 hourly for near-surface data (e.g. BLHs) and 6 hourly for three-dimensional
(3-D)
fields (e.g. temperature, wind, humidity, and convection).
Tracers in TM5 are transported by advection (in both horizontal and vertical
directions), cumulus convection, and vertical diffusion. Tracer advection is
based on the so-called “slopes scheme”, which considers a tracer mass
within a grid cell as a mean concentration and the spatial gradient of the
concentration within the grid box (Russel and Lerner, 1981), which is caused
by the motion of the tracer into and out of the grid box. Non-resolved
transport by shallow cumulus and deep convection in TM5 is parameterised by a
bulk mass flux approach originally described in Tiedtke (1989). Such
convective clouds are described by single pairs of entraining/detraining
plumes representing the updraft/downdraft motion. The parameterisation of the
vertical turbulent diffusion in the boundary layer is based on the scheme of
Holtslag and Moeng (1991), while the formulation of Louis (1979) is
considered in the free troposphere. The BLH is computed by using the
expression of Vogelezang and Holtslag (1996), as described in Sect. 2.1. The
exchange coefficients from the vertical diffusion are combined with the
vertical convective mass fluxes to calculate the sub-grid scale vertical
tracer transport. After redistributing the tracer mass by convection and
diffusion, the slopes are updated.
Recently, van der Veen (2013) proposed a revised scheme to update the
slopes. This “revised slopes scheme” results in enhanced horizontal
transport in TM5 by increasing the horizontal diffusivity of the numerical
scheme of the convection routine. Van der Veen (2013) found an improvement
of the inter-hemispheric mixing gradient in TM5, which was initially
underestimated as reported in, e.g., Patra et al. (2011). This “revised
slopes scheme” has been used for the sensitivity tests described below.
Furthermore, we performed sensitivity tests using directly the convection
fields from the ECMWF IFS model, instead of the default convection scheme
based on Tiedtke (1989). The ECMWF convection scheme includes several
improvements of the parameterisations of deep convection, radiation, clouds,
and orography, introduced operationally since the ECMWF ERA-15 analyses (e.g.
Gregory et al., 2000; Jakob and Klein, 2000; Morcrette et al., 2001).
Finally, we evaluate the combination of the “revised slopes scheme” and
the use of ECMWF convection fields.
TM5 boundary layer height scheme
In the TM5 model, the full expression of Vogelezang and Holtslag (1996) is
used to compute Rib, (Eq. 1). First, Rib is computed
at each model level by using the Eq. (1). The vertical profile of
Rib is then linearly interpolated between consecutive levels of
the model. The BLH is defined as the height, where Rib reaches
the Ric. In TM5, Ric is set to 0.3, and the minimum
BLH is set to 100 m.
For consistent comparison with the IGRA data, we calculate the BLH in TM5
also based on the definition of Seidel et al. (2012) as used in the InGOS
model validation exercise (i.e. Ric=0.25 and both surface wind and
friction velocity are set to zero in Eq. 1; see Sect. 2.1). Furthermore,
because InGOS and IGRA sites are not co-located, we extract the BLH in the
model both at the location of the InGOS station and at the location of the
nearest IGRA station, resulting in two sets of modelled BLHs labelled by the
following acronyms:
“TM5_INGOS”: BLHs extracted at the InGOS station
“TM5_INGOS_IGRA”: BLHs extracted at the IGRA
station, which is closest to the selected InGOS station.
In both cases, we use a 2-D interpolation (longitude/latitude) to
the location of the (InGOS or IGRA) station.
Furthermore, we also extract the default TM5 BLH (both at the InGOS and IGRA
station) and the BLHs from ECMWF reanalyses. In general, the difference
between the BLH based on Seidel et al. (2012) and the TM5 default and ECMWF
BLHs are very small. Therefore, the latter are only shown in the Supplement
(Figs. S2–S11).
InGOS 222Rn flux map
We use the new 222Rn flux map developed by Karstens et al. (2015)
within the InGOS project (called hereafter “InGOS 222Rn flux map”).
This map is based on a parameterisation of 222Rn production and
transport in the soil, using a deterministic model based on the equations of
continuity and diffusion (Fick's first law) to compute the transport of
the 222Rn flux from the soil to the atmosphere. The modelled radon flux
is dependent on soil porosity and moisture, with the latter obtained from
two different soil moisture data sets, i.e. from the Land Surface Model
Noah (driven by NCEP-GDAS meteorological reanalysis and part of the Global
Land Data Assimilation System (GLDAS); Rodell et al., 2004) and from the
ERA-Interim/Land reanalysis, respectively. Karstens et al. (2015) found that
the flux estimates based on the GLDAS Noah soil moisture model on average
better represent observed fluxes. Therefore, we apply in this study the
222Rn flux map version based on the Noah soil moisture data set.
Furthermore, the 222Rn flux map considers the water table (from a
hydrological model simulation), the distribution of the 226Ra content
in the soil, and the soil texture. For comparison, we also apply the
commonly used constant emission maps with uniform continental 222Rn
exhalation of 21.98 mBq m-2 s-1 between 60∘ S and
60∘ N; uniform continental 222Rn emissions of 11.48 mBq m-2 s-1 between 60 and 70∘ N (excluding
Greenland); and zero flux elsewhere (Jacob et al., 1997). The InGOS
222Rn flux map provides monthly 222Rn fluxes over the 2006–2011
period, aggregated to a 0.5∘× 0.5∘ grid for
Europe and complemented by the constant emissions for the regions outside
Europe. Figure 2a and b illustrate the spatial and mean seasonal
variations of the 222Rn fluxes from the InGOS 222Rn flux map over
Europe. The modelled 222Rn flux is found to be larger in the areas
where the 226Ra activity concentration in the upper soil is very high,
such as the Iberian Peninsula, areas in Central Italy and the Massif Central
in southern France (Fig. 2a). The mean seasonal variations of the
222Rn fluxes are mainly driven by the soil moisture. On average, the
InGOS 222Rn emissions over Europe are smaller than the constant
emission (except July–September; Fig. 2b).
Radon (222Rn) emissions used for the model simulations,
(a) spatial distribution of InGOS emissions over Europe during
July 2009, (b) seasonal and inter-annual variations of InGOS
emissions (in different colours for different years; mean in red) and the
commonly used constant emissions (black). The mean seasonal variations are
averaged over the geographic domain between 10∘ W and 30∘ E
longitude and between 35 and 70 ∘N latitude.
Simulated 222Rn activity concentrations
We simulate 222Rn activity concentrations using either the InGOS
222Rn flux map based on Noah soil moisture data, or constant 222Rn
fluxes (see Sect. 3.3). Furthermore, we also apply the revised slopes
scheme and the updated convection scheme based on ECMWF reanalyses (see Sect. 3.1) for the InGOS 222Rn flux-map-based simulations only. These
different simulations are labelled by the following acronyms:
FC_CT: constant 222Rn fluxes, and default convection
scheme in TM5 based on Tiedtke (1989)
FI_CT: InGOS 222Rn flux map, and default convection
FI_CU: InGOS 222Rn flux map by using both the
“revised slopes scheme” and the convection scheme based on ECMWF reanalyses
We also analysed the use of revised slopes scheme and the updated
convection scheme independently (see Supplement; Figs. S14–S24)
The model simulations are 3-D linearly interpolated (i.e. horizontally and
vertically) to the location of the station, and averaged over 1 h.
Observed (IGRA; blank) and modelled (TM5_INGOS; red and
TM5_INGOS_IGRA; orange) BLHs for InGOS stations at 00:00 UTC (2006–2010).
The titles of each panel show the names and acronyms of the InGOS station,
and the names of the nearest IGRA station used for comparison. The whisker
plots show the monthly minimum and maximum values (bars), and the 25 and
75 % percentiles (boxes). The median values are given by the horizontal
line and the mean values by the open circles in the boxes. The different
acronyms of the model data are defined in Sect. 3.2 of the text.
As Fig. 3, but at 12:00 UTC.
ResultsSimulated boundary layer heights vs. observations
We focus the analysis on the InGOS stations (measuring CH4 and
N2O, and/or 222Rn activity concentrations; Fig. 1) at low
altitudes (i.e. excluding mountain stations) and compare the modelled BLHs
with observations at the closest IGRA stations. Figures 3 and 4 show the
mean seasonal variation for the nocturnal (00:00 UTC) and daytime (12:00 UTC) BLH,
respectively (2006–2010 average). The nocturnal BLHs show a clear seasonal
cycle at most stations, with typically higher nocturnal BLHs during winter
(but also larger range between 25 and 75 % percentile) compared to
summer. This seasonal pattern is very consistent between measurements and
model simulations. However, at some continental stations (e.g. Heidelberg,
Gif-sur-Yvette) the IGRA data show very low nocturnal BLHs (median value
below 100 m) during summer, which are not reproduced by the model. In
general, the whisker plots (Fig. 3) show a skewed (non-normal)
distribution for most monthly data (observations and model simulations) with
the median value being usually significantly lower than the mean. The
daytime BLHs show a very pronounced seasonal cycle at most continental
stations (opposite in phase with the seasonal cycle of the nocturnal BLH),
with typical values around 500 m during winter, and ∼ 1000–2000 m during summer. The daytime BLH is in general relatively well
simulated at most stations, as further illustrated by the ratios between
modelled and observed BLHs, which are close to 1 (see Fig. 8). An
exception, however, are coastal sites (e.g. Angus, Mace Head), where
apparently the model representation errors (e.g. transition between land
and sea) are a limiting factor. In general, it should be expected that the
model BLH extracted at the location of the IGRA station should agree better
than that extracted at the InGOS station (see Sect. 3.2 for the definition
of the model BLHs). However, e.g., at Egham, the opposite is the case, since
the IGRA station (Herstmonceaux) is closer to the coast, and the
corresponding model BLH has more “marine” character (and the transition zone
between sea and land is not resolved by the model). For most stations far
from the coast, however, the difference between the BLH at the InGOS station
and the IGRA station is usually very small (Figs. 3, 4, and S2–S11). Compared to the data for the nocturnal BLH, the
daytime BLHs show much smaller difference between median and mean value,
indicating a less skewed frequency distribution (Figs. 3 and 4).
As in Fig. 3, but on the top Cabauw (CBW) where both ceilometer and
nearby IGRA observations (from De Bilt) are available. Observed (IGRA in
blank; ceilometer in grey) and simulated (colours) boundary layer heights at
12:00 UTC and for 2010 are shown. On the bottom, Traînou (TRN) lidar-based
boundary layer heights (grey) at 12:00 UTC during 2011 are shown. The model
boundary layer heights are represented by the coloured boxes (for the
different acronyms see Sect. 3.2).
In the Supplement (Figs. S2 to S11) we show the full time series for the
10 stations in 2009, illustrating that also the synoptic variability of the
BLH is relatively well reproduced by the models (for both nocturnal and
daytime BLH). Furthermore, we extend the analysis by using all IGRA stations
over Europe (about 130 stations; see Figs. 1, S12, and S13). This extended analysis confirms the major findings discussed
above, especially (1) the good agreement between simulated and observed BLH
during daytime, (2) the tendency for the simulated nocturnal BLHs to be too
high during summer, and (3) larger differences between TM5 and IGRA BLHs for
stations located close to the coasts.
In the following we include the ceilometer and lidar derived BLH at Cabauw
and Traînou, respectively, in the analysis. As clearly visible from the
correlation plot between ceilometer and IGRA data for Cabauw (Fig. S1),
the ceilometer BLHs during midday are usually lower than the IGRA data
(especially for the period March to September), while modelled BLHs fall in
between the two observational data sets (Fig. 5). Part of this difference
is likely due to the different methodologies. Hennemuth and Lammert (2006)
pointed out that inconsistencies between the atmospheric thermal profile and
the aerosol concentration profile can result in differences between
radiosonde and lidar/ceilometer BLH retrievals. In addition, the spatial
separation between Cabauw and De Bilt (∼ 23 km) combined with
different surface characteristics (wetter soils in Cabauw and different
large scale surface roughness) may play some role. While the correlation
between IGRA BLHs and the ceilometer BLH retrievals at Cabauw is reasonable
(r=0.63) during daytime, it is very poor during night (Fig. S1),
probably due to the issues of ceilometers to detect the shallow nocturnal
BLH, as mentioned in Sect. 2.1.2. The lidar daytime data at Traînou for
2011 agree relatively well with the model BLHs (except May) (Fig. 5).
While no IGRA data are available for this period, the comparison between
model simulations and IGRA for 2006–2010 at Traînou (Fig. 4) shows a similar
(or slightly better) agreement as the comparison between lidar and model for
2011.
Seasonal variations of daily maximum of observed and simulated radon
(222Rn) activity concentrations at InGOS sites at 05:00 UTC
(2006–2011). The whisker plots show the monthly minimum and maximum values
(bars), and the 25 and 75 % percentiles (boxes). The median values are
given by the horizontal line and the mean values by the open circles in the
boxes. The observed radon activity concentrations are shown in blank, and the
model simulations are represented by the coloured boxes (the acronyms for the
different model simulations are defined in Sect. 3.4). FC uses constant
222Rn fluxes and FI the InGOS flux map.
As in Fig. 6, but at 14:00 UTC illustrating the seasonal
variations of daily minimum of radon (222Rn) activity concentrations.
Left: statistics of observed vs. simulated 222Rn activity
concentrations for the different stations (12:00 UTC). Right: statistics of
observed (IGRA (•) and ceilometer (CEIL)/lidar (∗)) vs.
simulated boundary layer heights (TM5_INGOS_IGRA) (12:00 UTC). The
acronyms of the stations (x axis) are given in Table 1. For the median and
rms values, the units are given on the top of the two columns.
Simulated 222Rn activity concentrations vs. observations
Figures 6 and 7 show the mean seasonal variations of observed and simulated
222Rn activity concentrations at each of the studied InGOS sites at
05:00 UTC (time around which typically the daily maximum 222Rn activity
concentration occurs) and at 14:00 UTC (222Rn daily minimum),
respectively. For most stations, TM5 simulated 222Rn activity
concentrations based on the InGOS 222Rn flux map show significantly
better agreement with observations than the simulations based on the
constant 222Rn flux, especially regarding the average seasonal
variations. The improvement is largest during winter months, when TM5
simulations based on the constant 222Rn fluxes often overestimate
observations, while simulated concentrations based on the InGOS 222Rn
flux map are significantly lower owing to the lower 222Rn fluxes
(Figs. 6 and 7). This, in turn, is driven mostly by the higher soil
moisture and consequently lower permeability of the soil in winter.
Furthermore, large differences are visible at many northern European sites
close to the coast (Angus, Lutjewad, Mace Head, Cabauw), where the water
table can be very shallow, significantly reducing the 222Rn fluxes
(Karstens et al., 2015). Model simulations based on the InGOS 222Rn
flux map (which include modelled water table in the parameterisation of
222Rn fluxes) agree much better with observations than the control runs
with constant 222Rn fluxes. Despite the larger 222Rn fluxes during
summer, daily minimum 222Rn concentrations in the model and observations
are usually lower at continental stations (e.g. Heidelberg, Gif-sur-Yvette)
due to the much higher daytime boundary layer in summer compared to winter.
Figures S14 to S24 in the Supplement show the full time series of simulated
and observed 222Rn concentrations at the 10 studied InGOS stations
(with 222Rn activity concentration observations available) for 2009.
Seasonal variations of 222Rn activity concentrations and
boundary layer heights (BLHs) at the InGOS stations that measure 222Rn
activity concentrations. The observed concentrations are represented by the
black solid line with dots. Three model simulations are considered: FC_CT,
the model simulations using constant emissions; FI_CT using the InGOS
emissions and the default convection scheme of TM5; FI_CU using the InGOS
emissions and the combination of the “revised slopes scheme” and the new
convection scheme based on ECMWF reanalyses. The BLHs of TM5
(TM5_INGOS_IGRA) are in dark blue, while observed IGRA BLHs at 00:00 and
12:00 UTC are shown by the black diamonds together with their uncertainties.
The lidar BLHs at Traînou (for 2011) are shown by the light blue line.
The seasonal variations of the ratios of BLHs (TM5/IGRA; black dots
with error bars) at 12:00 UTC and the ratios of 222Rn activity
concentrations (OBS/TM5) at 12:00, 13:00, 14:00, and 15:00 UTC for the
four seasons (DJF, MAM, JJA, and SON) of the year 2009 for all InGOS 222Rn
measurement sites. The closest IGRA station to the radon measurement site is
considered (see Fig. 1). Three TM5 simulations are shown here: the model
simulations using the constant emissions (FC_CT; coloured diamond), InGOS
emissions and using the default convection scheme of TM5 (FI_CT; coloured
filled circles), and using the new convection scheme (FI_CU; coloured
triangles).
Relationship between 222Rn activity concentrations and boundary layer
heights
In the following, we analyse the relationship between 222Rn activity
concentration and BLH in more detail. Figure 9 shows the mean seasonal
diurnal cycle of observed and simulated 222Rn activity concentration and
BLH for the four seasons at different sites. The figure illustrates the very
strong anti-correlation between simulated BLH and 222Rn activity
concentration: The modelled BLHs increase sharply between 09:00 and
10:00 UTC (10:00/11:00 and 11:00/12:00 LT), resulting in an immediate
decrease of modelled 222Rn concentrations. In contrast, the 222Rn
activity concentration measurements show a slower decrease over several
hours. Although this slow decrease may be partially due to the slow (45 min)
response time of the two-filter detectors, it is clear that the sharp changes
in simulated BLHs and 222Rn activity concentrations are mainly due to
the relatively coarse temporal resolution of ECMWF meteorological data
(3 hourly for surface data (e.g. BLHs) and 6 hourly for 3-D fields
(temperature, wind, and humidity); see Sect. 3.1). Because the ceilometer data
at Cabauw during night might be questionable, we included in Fig. 9 only the
lidar measurements at Traînou (TR4). These show a much slower growth of the
BLH, starting in the morning and reaching its maximum in the late afternoon,
as also illustrated in Pal et al. (2012, 2015). Despite the obvious issue of
the temporal resolution of the model, however, inspection of Fig. 9 also
indicates significant mismatches between simulated and observed 222Rn
activity concentrations that cannot be explained wholly by problems with the
modelled BLH (even accounting for possible instrumental response time
effects). Especially during daytime, the TM5 BLHs are close to the IGRA
measurements at most stations (as also illustrated by the ratios of BLHs in
Fig. 8), whereas large differences are observed between the simulated and
measured 222Rn activity concentrations at several stations. This is
further illustrated in Fig. 10, where we compare the ratio of simulated to
observed BLH with the ratio of observed to simulated 222Rn activity
concentration during daytime for the different seasons. If the 222Rn
activity concentration errors were purely due incorrect dilutions resulting
from errors in the modelled BLH at a given station, the two ratios would be
similar. This is clearly not the case, however, and the modelled afternoon
concentration ratios range widely (from 0.2 to 1.8) from station to station.
These mismatches between observed and simulated 222Rn activity
concentrations may be related to shortcomings of TM5 in correctly simulating
the vertical 222Rn activity concentration gradients within the boundary
layer (see below). Furthermore, it is important to consider the uncertainties
of the InGOS 222Rn flux map. Karstens et al. (2015) estimated that the
most important uncertainty in the InGOS 222Rn flux is due to the
uncertainties in the soil moisture data. Altogether, the uncertainties in
modelled 222Rn fluxes for individual pixels
(0.083∘× 0.083∘) are estimated to be about
50 %. Karstens et al. (2015) pointed out that the uncertainty of the
222Rn fluxes averaged over the footprint of the measurements might be
smaller. However, the uncertainties of neighbouring pixels in the InGOS
222Rn flux map are likely to be strongly correlated, and therefore the
reduction of the relative uncertainty (integrated over a typical footprint of
the order of 50–200 km) is probably relatively small. Assuming an overall
uncertainty of ∼ 50 % of the regional 222Rn fluxes, the model
simulations could be considered broadly consistent with observations at most
sites.
Sensitivity of simulated 222Rn activity concentrations to convection
scheme
The use of the new ECMWF-based convection combined with the “revised slopes
scheme” (i.e. FI_CU acronym in Sect. 3.4) results in a small decrease of
simulated 222Rn concentrations at most stations, typically on the order
of ∼ 10-30 % (Figs. 6–9). However, root mean square (rms) and
correlation coefficients are very similar at most sites for both convection
parameterisations (Fig. 8). Hence, no clear conclusions can be drawn, which
parameterisation is more realistic. At the same time, Fig. 8 demonstrates
again the improvement using the InGOS 222Rn flux map, resulting in
(1) ratios between simulated and observed 222Rn activity concentration
closer to one, (2) lower rms, and (3) higher correlation coefficients at
several stations, compared to the model simulations using constant 222Rn
fluxes. This highlights the challenge to validate model simulations. The
difference of ∼ 10–30 % of 222Rn activity concentrations
using a different convection parameterisation is expected to result in a
difference of similar order of magnitude for the GHG emissions derived in
inverse modelling. The first GHG inversions with the new ECMWF-based convection
confirmed that derived emissions change significantly (not shown).
Comparison of simulated and observed 222Rn activity concentrations:
impact of sampling time
Figure 10 illustrates further that the ratio between observed and simulated
daytime 222Rn activity concentration also depends on the exact hour,
decreasing significantly between 12:00 and 15:00 UTC at several stations
(very pronounced at Traînou and Ispra). This is clearly due to the
shortcomings of TM5 to simulate the diurnal cycle in the BLH discussed above
(owing to the coarse temporal resolution of the meteorological data). In the
current TM5-4DVAR system the average (observed and simulated) concentrations
between 12:00 and 15:00 LT are used to derive emissions (Bergamaschi et al.,
2010, 2015). Given the too fast increase of the BLH and consequently too fast
decrease of simulated mixing ratios in the morning transition period, the
choice of the assimilation time window may introduce some systematic errors
in the flux inversions.
In the analyses shown in Fig. 10, the data include all stability regimes.
In addition, we performed this analysis separately for unstable, neutral,
and stable vertical mixing conditions. We used the bulk Richardson number
calculated at the first level of the model. This extended analysis, however,
showed relatively similar model performance for these different weather
conditions (results not shown). A limitation of this exercise is that for
both stable and neutral stability regimes, we had at most stations only few
cases per season.
Mean diurnal variations of the radon activity concentration
differences between the two measurement levels at Cabauw (20 m (CB1), 200 m
(CB4)). The observed gradient is shown by the black solid line with dots (for
each month of the year 2009), and the modelled gradient by the solid green
line for the constant emissions (FC_CT), by the solid red line for the InGOS
emissions (FI_CT), and by the solid orange line for the simulations using
the InGOS emissions and the combination of the “revised slopes scheme” and
the new convection scheme based on ECMWF reanalyses (FI_CU), respectively.
Vertical gradients of 222Rn activity concentrations in the boundary
layer at Cabauw
Finally, we explore the vertical gradients of TM5 simulated 222Rn
activity concentrations at Cabauw, where measurements are available at two
vertical levels (20 m (CB1) and 200 m (CB4) height; Table 1). The
measurement height of 20 m is within the first model layer, while 200 m is
within layer 3. Figure 11 shows the monthly mean diurnal variations of
modelled and observed vertical gradients of 222Rn activity concentrations
for each month for 2009. Although the InGOS 222Rn flux-based model
simulations agree better with observations (in terms of 222Rn activity
concentrations; see Figs. 6, 7, and 8) compared to the model simulations
based on constant fluxes, this is not the case for the 222Rn gradients
for some months: between June and November the modelled gradients based on
the constant fluxes agree better with observations, which could point to
partially compensating systematic errors (e.g. too high 222Rn fluxes
might be compensated by too fast vertical mixing). During large parts of the
year, the InGOS 222Rn flux-based model simulations underestimate the
observed gradients. This is further illustrated in the scatter plots shown in
Fig. 12 (separately for 00:00 and 12:00 UTC). For inverse modelling,
especially the underestimated vertical gradient during daytime is critical
and could lead to biases in the GHG inversions. Furthermore, Figure 11 shows
that during the transition phase in the morning the modelled 222Rn
activity concentration vertical gradient decreases faster than the observed
gradient, which is probably largely due to the coarse time resolution of the
meteorological data in TM5 together with the slow response time of the
two-filter radon measurements, although it may also indicate that vertical
mixing is proceeding too rapidly in the model.
Correlation plots between the simulated (“MOD”) and observed
(“OBS”) vertical 222Rn activity concentration gradients (difference
between 20 m (CB1) and 200 m (CB4) at Cabauw at 00:00 UTC (top) and
12:00 UTC (bottom)). Model simulations using InGOS emissions (FI_CT) are
shown. Each colour indicates the month at which the data are obtained.
Conclusions
In the first part of this study, we evaluated the boundary layer dynamics of
the TM5 model by comparison with BLHs from the NOAA IGRA radiosonde data as
well as with BLH retrievals from a ceilometer at Cabauw and lidar at
Traînou.
TM5 reproduces reasonably well the IGRA BLHs during daytime within 10–20 %
(which is within the uncertainty of the IGRA data) for continental stations
at low altitudes. During night, the model overestimates the shallow
nocturnal BLHs, especially for very low BLHs (< 100 m) observed
during summer time. At coastal sites, the differences between simulated BLH
and IGRA observations (both day and nighttime) are usually larger due to
model representation errors (since the transition zone between the marine
boundary layer over sea and the continental boundary layer over land is not
resolved by the model).
The BLH retrievals at Cabauw show a reasonable correlation with IGRA data
from De Bilt at 12:00 UTC, but are systematically lower. During night (00:00 UTC),
however, the two data set show only a very poor correlation. Besides the
fundamental differences in the BLH retrieval methods, however, also the
spatial separation between Cabauw and De Bilt (∼ 23 km)
probably contributes to the differences in the derived BLH. For the lidar
BLH data from Traînou, no direct comparison with the IGRA data is available
(due to different time periods), but the comparison with the modelled BLH
show similar agreement with the two different observational data sets (IGRA:
for 2006–2010; lidar: 2011). For the better exploitation of ceilometer/lidar data in the future, the further development of BLH retrievals is
essential to ensure consistency between the different methods.
In the second part of this study, we compared TM5 simulations of 222Rn
activity concentrations with quasi-continuous 222Rn measurements from
10 European monitoring stations. The 222Rn activity concentration
simulations based on the new 222Rn flux map show significant
improvements compared to 222Rn simulations using constant 222Rn
fluxes, especially regarding the average seasonal variability and generally
lower simulated 222Rn activity concentrations at northern European sites
close to the coast. These improvements highlight the benefit of the
process-based approach, including a parameterisation of the water table
(Karstens et al., 2015). Nevertheless, the (relative) differences between
simulated and observed daytime minimum 222Rn concentrations are larger
for several stations (of the order of 50 %) than the (relative)
differences between simulated and observed BLH at noon. This is probably
partly related to the uncertainties in the 222Rn flux map (estimated to
be of the order of 50 %). In addition, however, also potential
shortcomings of TM5 to correctly simulate the vertical 222Rn activity
concentration gradients are likely to play a significant role, which may be
caused by the vertical diffusion coefficients and/or the limited vertical
resolution in the model.
The comparison of simulated 222Rn activity concentrations with
measurements at Cabauw (20 m vs. 200 m) shows that the model
underestimates the measured vertical gradient (i.e. differences of
concentrations between 20 and 200 m levels) at this station. Furthermore,
the sharp increase of the modelled BLH in the morning transition period
results in a rapid decrease of the simulated 222Rn activity
concentrations, while 222Rn measurements show a slower decrease at many
stations. Although this latter timing effect may be partially due to the
slow (45 min) response time of the two-filter radon detectors, it is clear
that the current coarse temporal resolution of the TM5 meteorological data
(3 hourly for surface data and 6 hourly for 3-D fields) limits the capability
of simulating the diurnal cycle realistically. These issues probably lead to
systematic biases in inversions of GHG emissions. An updated TM5-4DVAR
system is currently under development with increased temporal resolution of
the meteorological data (3-hourly ECMWF data, interpolated to observational
data time).
Finally, we evaluated the revised slopes scheme and the new ECMWF-based
convection scheme in the TM5 model. The results show a relatively small
impact of the new slopes treatment, but a significant impact of the new ECMWF
convection scheme, leading to significantly lower 222Rn activity
concentrations (about 20 %) during daytime, especially in winter. While
this is expected to have a significant impact on derived emissions in GHG
inversions, the comparison with the available European 222Rn activity
concentration observations showed very similar performance. Hence, no clear
conclusion about which parameterisation is more realistic can be drawn from
this study. These findings highlight the challenges of validating atmospheric
transport models with the accuracy required to better evaluate and improve
the quality of GHG flux inversions. In order to improve the validation
capabilities it would be important (1) to increase the number of 222Rn
monitoring stations, (2) to perform vertical 222Rn activity
concentration profile measurements at tall towers and also from aircraft
(e.g. Chambers et al., 2011; Williams et al., 2011, 2013), (3) to extend the
validation of the 222Rn inventories by local/regional 222Rn flux
measurements, (4) to further develop the BLH retrievals from
ceilometer/lidar instruments, and
(5) to further extend the ceilometer/lidar network. More work is also needed to improve the representation of
the nocturnal boundary layer in global and regional models. The use of
222Rn in the diagnosis of the nocturnal mixing effects is one area
showing promise in this regard (Williams et al., 2013).
Code and data availability
Further information about the TM5 code can be found at
http://tm5.sourceforge.net/. Readers interested in the TM5 code can contact
Maarten Krol (maarten.krol@wur.nl), Arjo Segers (arjo.segers@tno.nl) or
Peter Bergamaschi (peter.bergamaschi@jrc.ec.europa.eu). Model output are available upon
request.
The Supplement related to this article is available online at doi:10.5194/gmd-9-3137-2016-supplement.
Acknowledgements
This work has been supported by the European Commission Seventh Framework
Programme (FP7/2007–2013) project InGOS under grant agreement 284274. We
thank Juha Hatakka for providing 222Rn data from Pallas. Furthermore, we
are grateful to Clemens Schlosser from the German Federal Office for
Radiation Protection for the 222Rn data from Schauinsland, which were
used for additional analyses. ECMWF meteorological data have been preprocessed
by Philippe Le Sager into the TM5 input format. We are grateful to ECMWF for
providing computing resources under the special project “Global and Regional
Inverse Modeling of Atmospheric CH4 and N2O (2012–2014)” and
“Improve estimates of global and regional CH4 and N2O emissions
based on inverse modelling using in situ and satellite measurements
(2015–2017)”.
Edited by: S. Remy
Reviewed by: three anonymous referees
References
Angevine, W., Grimsdell, A., Hartten, L. M., and Delany, A. C.: The Flatland
Boundary Layer Experiments, B. Am. Meteorol. Soc., 79, 419–431, 1998.Bergamaschi, P., Krol, M., Meirink, J. F., Dentener, F., Segers, A., van
Aardenne, J., Monni, S., Vermeulen, A., Schmidt, Ramonet, M., Yver, C.,
Meinhardt, F., Nisbet, E. G., Fisher, R., O'Doherty, S., and Dlugokencky, E.
J.: Inverse modeling of European CH4 emissions 2001–2006, J. Geophys.
Res., 115, D22309, 10.1029/2010JD014180, 2010.Bergamaschi, P., Corazza, M., Karstens, U., Athanassiadou, M., Thompson, R.
L., Pison, I., Manning, A. J., Bousquet, P., Segers, A., Vermeulen, A. T.,
Janssens-Maenhout, G., Schmidt, M., Ramonet, M., Meinhardt, F., Aalto, T.,
Haszpra, L., Moncrieff, J., Popa, M. E., Lowry, D., Steinbacher, M., Jordan,
A., O'Doherty, S., Piacentino, S., and Dlugokencky, E.: Top-down estimates of
European CH4 and N2O emissions based on four different inverse
models, Atmos. Chem. Phys., 15, 715–736, 10.5194/acp-15-715-2015, 2015.
Biraud, S., Ciais, P., Ramonet, M., Simmonds, P., Kazan, V., Monfray, P.,
O'Doherty, S., Spain, T. G., and Jennings, S. G.: European greenhouse gas
emissions estimated from continuous atmospheric measurements and radon 222 at
Mace Head, Ireland, J. Geophys. Res., 105, 1351–1366, 2000.Broquet, G., Chevallier, F., Bréon, F.-M., Kadygrov, N., Alemanno, M.,
Apadula, F., Hammer, S., Haszpra, L., Meinhardt, F., Morguí, J. A.,
Necki, J., Piacentino, S., Ramonet, M., Schmidt, M., Thompson, R. L.,
Vermeulen, A. T., Yver, C., and Ciais, P.: Regional inversion of CO2
ecosystem fluxes from atmospheric measurements: reliability of the
uncertainty estimates, Atmos. Chem. Phys., 13, 9039–9056,
10.5194/acp-13-9039-2013, 2013.Carouge, C., Bousquet, P., Peylin, P., Rayner, P. J., and Ciais, P.: What can
we learn from European continuous atmospheric CO2 measurements to
quantify regional fluxes – Part 1: Potential of the 2001 network, Atmos.
Chem. Phys., 10, 3107–3117, 10.5194/acp-10-3107-2010, 2010.Chambers, S., Williams, A. G., Zahorowski, W., Griffiths, A., and Crawford,
J.: Separating remote fetch and local mixing influences on vertical radon
measurements in the lower atmosphere, Tellus, 63B, 843–859,
10.1111/j.1600-0889.2011.00565.x, 2011.Chevillard, A., Ciais, P., Karstens, U., Heimann, M., Schmidt, M., Levin, I.,
Jacob, D., Podzun, R., Kazan, V., Sartorius, H., and Weingartner, E.:
Transport of 222Rn using the regional model REMO: a detailed comparison
with measurements over Europe, Tellus B, 54, 850–871, 2002.Collaud Coen, M., Praz, C., Haefele, A., Ruffieux, D., Kaufmann, P., and
Calpini, B.: Determination and climatology of the planetary boundary layer
height above the Swiss plateau by in situ and remote sensing measurements as
well as by the COSMO-2 model, Atmos. Chem. Phys., 14, 13205–13221,
10.5194/acp-14-13205-2014, 2014.Corazza, M., Bergamaschi, P., Vermeulen, A. T., Aalto, T., Haszpra, L.,
Meinhardt, F., O'Doherty, S., Thompson, R., Moncrieff, J., Popa, E.,
Steinbacher, M., Jordan, A., Dlugokencky, E., Brühl, C., Krol, M., and
Dentener, F.: Inverse modelling of European N2O emissions: assimilating
observations from different networks, Atmos. Chem. Phys., 11, 2381–2398,
10.5194/acp-11-2381-2011, 2011.Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P.,
Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N.,
Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S.
B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J.,
Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and
Vitart, F.: The ERA-Interim reanalysis: configuration and performance of the
data assimilation system, Q. J. Roy. Meteor. Soc., 137, 553–597,
10.1002/qj.828, 2011.
Denning, A. S., Holzer, M., Gurney, K. R., Heimann, M., Law, R. M., Rayner,
P. J., Fung, I. Y., Fan, S.-M., Taguchi, S., Friedlingstein, P., Balkanski,
Y., Taylor, J., Maiss, M., and Levin, I.: Three-dimensional transport and
concentration of SF6: A model intercomparison study (TransCom 2), Tellus,
51B, 266–297, 1999.
Dentener, F., Feichter, J., and Jeuken, A.: Simulation of the transport of
Rn222 using on-line and off-line global models at different horizontal
resolutions: a detailed comparison with measurements, Tellus, 51B, 573–602,
1999.Eresmaa, N., Karppinen, A., Joffre, S. M., Räsänen, J., and Talvitie,
H.: Mixing height determination by ceilometer, Atmos. Chem. Phys., 6,
1485–1493, 10.5194/acp-6-1485-2006, 2006.Ganesan, A. L., Manning, A. J., Grant, A., Young, D., Oram, D. E., Sturges,
W. T., Moncrieff, J. B., and O'Doherty, S.: Quantifying methane and nitrous
oxide emissions from the UK and Ireland using a national-scale monitoring
network, Atmos. Chem. Phys., 15, 6393–6406, 10.5194/acp-15-6393-2015,
2015.Gerbig, C., Lin, J. C., Wofsy, S. C., Daube, B. C., Andrews, A. E., Stephens,
B. B., Bakwin, P. S., and Grainger, C. A.: Toward constraining regional-scale
fluxes of CO2 with atmospheric observations over a continent:
1. Observed spatial variability from airborne platforms, J. Geophys. Res.,
108, 4756, 10.1029/2002JD003018, 2003.
Gregory, D., Morcrette, J.-J., Jakob, C., Beljaars, A. M., and Stockdale, T.:
Revision of convection, radiation and cloud schemes in the ECMWF model, Q. J.
Roy. Meteor. Soc., 126, 1685–1710, 2000.Griffiths, A. D., Parkes, S. D., Chambers, S. D., McCabe, M. F., and
Williams, A. G.: Improved mixing height monitoring through a combination of
lidar and radon measurements, Atmos. Meas. Tech., 6, 207–218,
10.5194/amt-6-207-2013, 2013.Haeffelin, M., Angelini, F., Morille, Y., Martucci, G., Frey, S., Gobbi, G.
P., Lolli, S., O'Dowd, C. D., Sauvage, L., Xueref. Remy, I., Wastine, B., and
Feist, D. G.: Evaluation of mixing-height retrievals from automatic profiling
lidars and ceilometers in view of future integrated networks in Europe,
Bound.-Layer Meteorol., 143, 49–75, 10.1007/s10546-011-9643-z, 2012.Haij, M. J. de., Wauben, W. M. F., and Baltink, H. K.: Determination of
mixing layer height from ceilometer backscatter profiles, edited by: Slusser,
J. R., Schäfer, K., and Comerón, A., Stockholm, Zweden, Proc. SPIE,
6362, 63620R, 10.1117/12.691050, 2006.
Hatakka, J., Aalto, T., Aaltonen, V., Aurela, M., Hakola, H., Kompula, M.,
Laurila, T., Lihavainen, H., Paatero, J., Salminen, K., and Viisanen, Y.:
Overview of the atmospheric research activities and results at Pallas GAW
station, Boreal Environ. Res., 8, 365–383, 2003.
Hennemuth, B. and Lammert, A.: Determination of the convective boundary layer
height from radiosonde and lidar backscatter, Bound.-Lay. Meteorol., 120,
181–209, 2006.
Holtslag, A. A. M. and Moeng, C. H.: Eddy diffusivity and counter-gradient
transport in the convective atmospheric boundary layer, J. Atmos. Sci., 48,
1690–1698, 1991.
Jacob, D. J. and Prather, M. J.: Radon-222 as a test of convective transport
in a general circulation model, Tellus B, 42, 118–134, 1990.Jacob, D. J., Prather, M. J., Rasch, P. J., Shia, R.-L., Balkanski, Y.
J.,Beagley, S. R., Bergmann, D. J., Blackshear, W. T., Brown, M., Chiba, M.,
Chipperfield, M. P., de Grandpré, J., Dignon, J. E., Feichter, J.,
Genthon, C., Grose, W. L., Kasibhatla, P. S., Köhler, I., Kritz, M. A.,
Law, K., Penner, J. E., Ramonet, M., Reeves, C. E., Rotman, D. A., Stockwell,
D. Z., Van Velthoven, P. F. J., Verver, G., Wild, O., Yang, H., and
Zimmermann, P.: Evaluation and intercomparison of global atmospheric
transport models using 222Rn and other short-lived tracers, J. Geophys. Res.,
102, 5953–5970, 10.1029/96JD02955, 1997.
Jakob, C. and Klein, S. A.:, A parametrization of cloud and precipitation
overlap effects for use in General Circulation Models, Q. J. Roy. Meteor.
Soc., 126, 2525–2544, 2000.Karstens, U., Bergamaschi, P., Levin, I., Heard, I., Manning, A. J., Saunois,
M., Vermeulen, A. T., Koffi, E., Locatelli, R., Schmidt, M., Fisher, R.,
Hatakka, J., Meijer, H. A. J., Moncrieff, J., Schlosser, C., Pal, S., and
Ramonet, M.: Validation of atmospheric transport models through comparisons
with 222Radon and boundary layer mixing height observations, Seventh
International Symposium on Non-CO2 Greenhouse Gases (NCGG7),
5–7 November 2014, Amsterdam, the Netherlands, 2014.Karstens, U., Schwingshackl, C., Schmithüsen, D., and Levin, I.: A
process-based 222radon flux map for Europe and its comparison to
long-term observations, Atmos. Chem. Phys., 15, 12845–12865,
10.5194/acp-15-12845-2015, 2015.Kort, E. A., Eluszkiewicz, J, Stephens, B. B., Miller, J. B., Gerbig, C.,
Nehrkorn, T., Daube, B. C., Kaplan, J. O., Houweling, S., and Wofsy, S. C.:
Emissions of CH4 and N2O over the United States and Canada based on
a receptor-oriented modeling framework and COBRA-NA atmospheric observations,
Geophys. Res. Lett., 35, L18808, 10.1029/2008GL034031, 2008.Krol, M., Houweling, S., Bregman, B., van den Broek, M., Segers, A., van
Velthoven, P., Peters, W., Dentener, F., and Bergamaschi, P.: The two-way
nested global chemistry-transport zoom model TM5: algorithm and applications,
Atmos. Chem. Phys., 5, 417–432, 10.5194/acp-5-417-2005, 2005.
Levin, I., Born, M., Cuntz, M., Langendörfer, U., Mantsch, S., Naegler,
T., Schmidt, M., Varlagin, A., Verclas, S., and Wagenbach, D.: Observations
of atmospheric variability and soil exhalation rate of Radon-222 at a Russian
forest site: Technical approach and deployment for boundary layer studies,
Tellus, 54B, 462–475, 2002.Locatelli, R., Bousquet, P., Hourdin, F., Saunois, M., Cozic, A., Couvreux,
F., Grandpeix, J.-Y., Lefebvre, M.-P., Rio, C., Bergamaschi, P., Chambers, S.
D., Karstens, U., Kazan, V., van der Laan, S., Meijer, H. A. J., Moncrieff,
J., Ramonet, M., Scheeren, H. A., Schlosser, C., Schmidt, M., Vermeulen, A.,
and Williams, A. G.: Atmospheric transport and chemistry of trace gases in
LMDz5B: evaluation and implications for inverse modelling, Geosci. Model
Dev., 8, 129–150, 10.5194/gmd-8-129-2015, 2015.Lopez, M., Schmidt, M., Yver, C., Messager, C., Worthy, D., Kazan, V.,
Ramonet, M., Bousquet, P., and Ciais, P.: Seasonal variation of N2O
emissions in France inferred from atmospheric N2O and Rn-222
measurements, J Geophys. Res.-Atmos., 117, D14103, 10.1029/2012jd017703,
2012.
Louis, J. F.: A parametric model of vertical eddy fluxes in the atmosphere,
Bound.-Lay. Meteorol., 17, 187–202, 1979.Manning, A. J., O'Doherty, S., Jones, A. R., Simmonds, P. G., and Derwent, R.
G.: Estimating UK methane and nitrous oxide emissions from 1990 to 2007 using
an inversion modeling approach, J. Geophys. Res., 116, D02305,
10.1029/2010JD014763, 2011.Meirink, J. F., Bergamaschi, P., and Krol, M. C.: Four-dimensional
variational data assimilation for inverse modelling of atmospheric methane
emissions: method and comparison with synthesis inversion, Atmos. Chem.
Phys., 8, 6341–6353, 10.5194/acp-8-6341-2008, 2008.Miller, S. M., Wofsy, S. C., Michalak, A. M., Kort, E. A., Andrews, A. E.,
Biraud, S., Dlugokencky, E., Eluszkiewicz, J., Fischer, M. L.,
Janssens-Maenhout, G., Miller, B. R., Miller, J. B., Montzka, S., Nehrkorn,
T., and Sweeney, C.: Anthropogenic emissions of methane in the United States,
P. Natl. Acad. Sci., 110, 20018–20022, 10.1073/pnas.1314392110, 2013.
Morcrette, J.-J., Mlawer, E. J., Iacono, M. J., and Clough, S. A.: Impact of
the radiation-transfer scheme RRTM in the ECMWF forecast system, Technical
report in the ECMWF Newsletter, Reading, UK, No. 91, 2001.
Pal, S.: Monitoring Depth of Shallow Atmospheric Boundary Layer to Complement
LiDAR Measurements Affected by Partial Overlap, Remote Sensing, 6,
8468–8493, 2014.Pal, S., Xueref-Remy, I., Ammoura, L., Chazette, P., Gibert, F., Royer, P.,
Dieudonné, E., Dupont, J.-C., Haeffelin, M., Lac, C., Lopez, M., Morille,
Y., and Ravetta, F.: Spatio-temporal variability of the atmospheric boundary
layer depth over the Paris agglomeration: An assessment of the impact of the
urban heat island intensity, Atmospheric environment, Elsevier, 63, 261–275,
10.1016/j.atmosenv.2012.09.046, 2012.Pal, S., Haeffelin, M., and Batchvarova, E.: Exploring a geophysical
process-based attribution technique for the determination of the atmospheric
boundary layer depth using aerosol lidar and near-surface meteorological
measurements, J. Geophys. Res.-Atmos., 118, 9277–9295,
10.1002/jgrd.50710, 2013.Pal, S., Lopez, M., Schmidt, M., Ramonet, M., Gibert, F., Xueref-Remy, I.,
and Ciais, P.: Investigation of the atmospheric boundary layer depth
variability and its impact on the 222Rn concentration at a rural site in
France, J. Geophys. Res.-Atmos., 120, 623–643, 10.1002/2014JD022322,
2015.Patra, P. K., Houweling, S., Krol, M., Bousquet, P., Belikov, D., Bergmann,
D., Bian, H., Cameron-Smith, P., Chipperfield, M. P., Corbin, K.,
Fortems-Cheiney, A., Fraser, A., Gloor, E., Hess, P., Ito, A., Kawa, S. R.,
Law, R. M., Loh, Z., Maksyutov, S., Meng, L., Palmer, P. I., Prinn, R. G.,
Rigby, M., Saito, R., and Wilson, C.: TransCom model simulations of CH4
and related species: linking transport, surface flux and chemical loss with
CH4 variability in the troposphere and lower stratosphere, Atmos. Chem.
Phys., 11, 12813–12837, 10.5194/acp-11-12813-2011, 2011.Rodell, M., Houser, P. R., Jambor, U., Gottschalck, J., Mitchell, K., Meng,
C.-J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin,
J. K., Walker, J. P., Lohmann, D., and Toll, D.: The Global Land Data
Assimilation System, B. Am. Meteorol. Soc., 85, 381–394,
10.1175/BAMS-85-3-381, 2004.
Russell, G. L. and Lerner, J. A.: A new finite-differencing scheme for the
tracer transport equation, J. Appl. Meteorol., 20, 1483–1498, 1981.Scheeren, H. A. and Bergamaschi, P.: First Three Years of CO2, CH4,
N2O, and SF6 Observations, and 222Radon-Based Emission
Estimates from the JRC Monitoring Station at Ispra (Italy): What Have We
Learned So Far?, Proceeding of the 16th WMO/IAEA Meeting on Carbon Dioxide,
Other Greenhouse Gases and Related Measurement Techniques (GGMT-2011),
Wellington, New Zeland, 25–28 October, 2011, World Meteorological
Organization, 2012.Schmidt, M., Lopez, M., Yver Kwok, C., Messager, C., Ramonet, M., Wastine,
B., Vuillemin, C., Truong, F., Gal, B., Parmentier, E., Cloué, O., and
Ciais, P.: High-precision quasi-continuous atmospheric greenhouse gas
measurements at Trainou tower (Orléans forest, France), Atmos. Meas.
Tech., 7, 2283–2296, 10.5194/amt-7-2283-2014, 2014.Schmithüsen, D., Chambers, S., Fischer, B., Gilge, S., Hatakka, J.,
Kazan, V., Neubert, R., Paatero, J., Ramonet, M., Schlosser, C., Schmid, S.,
Vermeulen, A., and Levin, I.: A European-wide 222Radon and 222Radon
progeny comparison study, Atmos. Meas. Tech. Discuss.,
10.5194/amt-2016-111, in review, 2016.
Seibert, P., Beyrich, F., Gryning, S. E., Joffre, S., Rasmussen, A., and
Tercier, P.: Review and intercomparison of operational methods for the
determination of the mixing height, Atmos. Environ, 34, 1001–1027, 2000.Seidel, D. J., Zhang, Y., Beljaars, A., Golaz, J.-C., Jacobson, A. R., and
Medeiros, B.: Climatology of the planetary boundary layer over the
continental United States and Europe, J. Geophys. Res.-Atmos, 117, D17106,
10.1029/2012JD018143, 2012.Smallman, T. L., Williams, M., and Moncrieff, J. B.: Can seasonal and
interannual variation in landscape CO2 fluxes be detected by atmospheric
observations of CO2 concentrations made at a tall tower?,
Biogeosciences, 11, 735–747, 10.5194/bg-11-735-2014, 2014.
Stull, R. B.: An Introduction to Boundary Layer Meteorology, Kluwer Academic
Publishers, Dordrecht, the Netherlands, 666 pp., 1988.Taguchi, S., Law, R. M., Rödenbeck, C., Patra, P. K., Maksyutov, S.,
Zahorowski, W., Sartorius, H., and Levin, I.: TransCom continuous experiment:
comparison of 222Rn transport at hourly time scales at three stations in
Germany, Atmos. Chem. Phys., 11, 10071–10084, 10.5194/acp-11-10071-2011,
2011.
Tiedtke, M.: A comprehensive mass flux scheme for cumulus parameterisation in
large scale models, Mon. Weather Rev, 177, 1779–1800, 1989.van der Laan, S., Karstens, U., Neubert, R. E. M., van der Laan-Luijkx, I.
T., and Meijer, H. A. J.: Observation-based estimates of fossil fuel-derived
CO2 emissions in the Netherlands using Delta 14C, CO and
222Radon, Tellus B, 62, 389–402, 10.1111/j.1600-0889.2010.00493.x,
2010.van der Veen, E.: Optimizing transport properties in TM5 using SF6,
Wageningen University, University of Twente, Master's thesis, Enschede, the
Netherlands, available at: http://essay.utwente.nl/65459/ (last access:
13 September 2016), 2013.Vermeulen, A. T., Hensen, A., Popa, M. E., van den Bulk, W. C. M., and
Jongejan, P. A. C.: Greenhouse gas observations from Cabauw Tall Tower
(1992–2010), Atmos. Meas. Tech., 4, 617–644, 10.5194/amt-4-617-2011,
2011.
Vogelezang, D. H. P. and Holtslag, A. A. M.: Evaluation and model impacts of
alternative boundary-layer height formulation, Bound.-Lay. Meteorol., 81,
245–269, 1996.
Whittlestone, S. and Zahorowski, W.: Baseline radon detectors for shipboard
use: Development and deployment in the First Aerosol Characterization
Experiment (ACE 1), J. Geophys. Res., 103, 743–751, 1998.
Williams, A. G., Zahorowski, W., Chambers, S. D., and Griffiths, A.: The
vertical distribution of radon in clear and cloudy daytime terrestrial
boundary layers, J. Atmos. Sci., 68, 155–174, 2011.Williams, A. G., Chambers, S. D., and Griffiths, A. D.: Bulk mixing and
decoupling of the nocturnal stable boundary layer characterized using a
ubiquitous natural tracer. Bound.-Lay. Meteorol., 20, 381–402,
10.1007/s10546-013-9849-3, 2013.Xia, Y., Sartorius, H., Schlosser, C., Stöhlker, U., Conen, F., and
Zahorowski, W.: Comparison of one- and two-filter detectors for atmospheric
222Rn measurements under various meteorological conditions, Atmos. Meas.
Tech., 3, 723–731, 10.5194/amt-3-723-2010, 2010.
Yver, C., Schmidt, M., Bousquet, P., Zahorowski, W., and Ramonet, M.:
Estimation of the molecular hydrogen soil up-take and traffic emissions at a
suburban site near Paris through hydrogen, carbon monoxide, and radon-222
semi-continuous measurements, J. Geophys. Res., 114, D18304,
10.1029/2009JD012122, 2009.
Zahorowski, W., Chambers, S. D., and Henderson-Sellers, A.: Ground based
radon-222 observations and their application to atmospheric studies, J.
Environ. Radioactiv., 76, 3–33, 2004.