Radon-222 (222Rn) is a short-lived radioactive gas
naturally emitted from land surfaces and has long been used to assess
convective transport in atmospheric models. In this study, we simulate
222Rn using the GEOS-Chem chemical transport model to improve our
understanding of 222Rn emissions and surface concentration seasonality
and characterize convective transport associated with two Goddard Earth
Observing System (GEOS) meteorological products, the Modern-Era Retrospective
analysis for Research and Applications (MERRA) and GEOS Forward Processing (GEOS-FP). We
evaluate four global 222Rn emission scenarios by comparing model
results with observations at 51 surface sites. The default emission scenario
in GEOS-Chem yields a moderate agreement with surface observations globally
(68.9 % of data within a factor of 2) and a large underestimate of winter
surface 222Rn concentrations at Northern Hemisphere midlatitudes and
high latitudes due to an oversimplified formulation of 222Rn emission
fluxes (1 atom cm-2 s-1 over land with a reduction by a factor of 3 under
freezing conditions). We compose a new global 222Rn emission scenario
based on Zhang et al. (2011) and demonstrate its potential to improve
simulated surface 222Rn concentrations and seasonality. The regional
components of this scenario include spatially and temporally varying
emission fluxes derived from previous measurements of soil radium content
and soil exhalation models, which are key factors in determining 222Rn
emission flux rates. However, large model underestimates of surface
222Rn concentrations still exist in Asia, suggesting unusually high
regional 222Rn emissions. We therefore propose a conservative
upscaling factor of 1.2 for 222Rn emission fluxes in China, which was
also constrained by observed deposition fluxes of 210Pb (a progeny of
222Rn). With this modification, the model shows better agreement with
observations in Europe and North America (> 80 % of data within
a factor of 2) and reasonable agreement in Asia (close to 70 %). Further
constraints on 222Rn emissions would require additional concentration
and emission flux observations in the central United States, Canada, Africa, and
Asia. We also compare and assess convective transport in model simulations
driven by MERRA and GEOS-FP using observed 222Rn vertical profiles in
northern midlatitude summer and from three short-term airborne campaigns.
While simulations with both GEOS products are able to capture the observed
vertical gradient of 222Rn concentrations in the lower troposphere (0–4 km), neither correctly represents the level of convective detrainment,
resulting in biases in the middle and upper troposphere. Compared with
GEOS-FP, MERRA leads to stronger convective transport of 222Rn, which
is partially compensated for by its weaker large-scale vertical advection,
resulting in similar global vertical distributions of 222Rn
concentrations between the two simulations. This has important implications
for using chemical transport models to interpret the transport of other
trace species when these GEOS products are used as driving meteorology.
Introduction
A reasonable representation of transport processes in global models is
critical to properly simulate tropospheric trace gases and aerosols.
However, convective transport and boundary-layer turbulent mixing occur at
sub-grid scales and are usually parameterized, unavoidably introducing
transport biases. Radon-222 (222Rn, half-life 3.8 d), an atmospheric
radionuclide, is an excellent tracer for assessing these biases due to its
relatively well-constrained sources and fairly simple sink pathway
(radioactive decay) in the atmosphere
(Jacob et al., 1997; Liu
et al., 1984). In this work, we evaluate and improve the simulation of
222Rn in a global chemical transport model (GEOS-Chem CTM) and assess
the role of convective transport in shaping its vertical distributions.
Radon-222 is an inert gas ubiquitously produced in soils and rocks by
radioactive decay of radium (226Ra). Shortly after 222Rn emanates
to the atmosphere, it decays to 210Pb (half-life 22.3 years). Wet and
dry depositions of 222Rn are negligible due to its inert nature. The
spatial distribution of 222Rn is therefore strongly affected by
convective and synoptic-scale transport. Numerous studies have used
222Rn to evaluate model transport processes, such as boundary-layer
structure and stability, vertical motion and mixing, and convection. For
instance, Liu et al. (1984) derived seasonal vertical eddy diffusion
coefficients using observed vertical profiles of tropospheric 222Rn
concentrations. Allen et al. (1996) used 222Rn profile measurements to
evaluate moist convection in their model and showed that deep convection
from the boundary layer to the upper troposphere facilitates the formation
of a “C-like” 222Rn vertical profile. Considine et al. (2005) used
222Rn and 210Pb measurements to examine the roles of convective
transport in three different meteorological datasets. K. Zhang et al. (2008)
tested two widely used convection schemes, Zhang–McFarlane–Hack
(Hack, 1994; Zhang and McFarlane, 1995) and
Tiedtke–Nordeng (Nordeng, 1994), in a global circulation model against
observed 222Rn profiles. Although model results with both schemes
showed similarly reasonable estimates of surface 222Rn concentrations,
some discrepancies were found in the middle and upper troposphere.
222Rn has also been used as an indicator of continental influences on
remote marine regions (Balkanski et al., 1992; Chambers et al., 2013, 2018).
Model intercomparison of simulated 222Rn distributions has been an
efficient approach to compare and contrast transport characteristics with
respect to boundary-layer turbulent mixing and convection
(Genthon and Armengaud,
1995; Jacob et al., 1997).
222Rn emission fluxes have been estimated based on (1) direct
measurements, usually by assuming a linear increase of 222Rn in a
chamber placed on soil, and (2) indirect estimates, through an integration
of 222Rn profiles by assuming a local balance between 222Rn
emission and decay. Using both approaches,
Wilkening et al. (1972) and Wilkening and Clements (1975) derived
an estimate of global mean 222Rn emission fluxes (0.75 atom cm-2 s-1
over land). Turekian et al. (1977) later suggested this global mean flux
rate was likely an underestimate due to the assumption of a local steady
state. By also considering one-dimensional longitudinal transport,
Turekian et al. (1977) recommended a higher global mean flux
of 1.2 atom cm-2 s-1, which led to a better agreement with observed
210Pb deposition fluxes across various latitudes. More recently, a mean
global emission flux of 1 atom cm-2 s-1 was considered more accurate, and
has been used uniformly over land as a standard configuration
(Balkanski et al., 1993). 222Rn fluxes
from water surface are a few orders of magnitude lower and can be neglected
compared with emissions over land (Wilkening and Clements,
1975). To date, most global models have used a globally uniform 222Rn
emission flux of 1 atom cm-2 s-1 with modifications in high latitudes
and for freezing soil temperatures.
Quantification of regional and temporal 222Rn emission variations has
been extended to broader areas and improved by new measurement techniques
and modeling approaches. Observations have indicated that local 222Rn
emission fluxes vary extensively with surface texture, soil moisture, radium
content, ice coverage, and freezing condition (Martell,
1985; Turekian et al., 1977). The increasing availability of observational
data inspired studies to quantify regional and temporal emission variations.
Based on a large collection of global observations,
Conen and Robertson (2002) proposed a linearly
decreasing gradient in the Northern Hemisphere, from 1 atom cm-2 s-1 at 30∘ N to 0.2 atom cm-2 s-1 at 70∘ N.
Regional and global 222Rn emission flux datasets at fine resolution
have also been developed based on models of gas diffusion in porous media;
this was facilitated by increasingly available soil parameters from
meteorological models and assimilation (or reanalysis) datasets.
Genthon and Armengaud (1995) introduced
soil parameters into a global general circulation model (GCM) to formulate online soil–atmosphere
exchange of 222Rn, which assisted in capturing rapid fluctuations of
surface 222Rn concentrations over freezing surfaces.
Zhuo et al. (2008) compiled radium content information from
over a thousand sites in China and constructed a high spatial resolution
emission map over the country. Hirao et al. (2010)
constructed a decade-long global 222Rn emission record based on
additional considerations about surface texture. Published 222Rn
emission inventories for Europe have very fine spatial resolutions (up to
0.083∘× 0.083∘) with monthly variability due
to extensive measurements of emission fluxes and surface concentrations
across the continent
(Karstens et
al., 2015; López-Coto et al., 2013; Szegvary et al., 2009). Such
variability is missing in the current GEOS-Chem standard model, which limits
the use of 222Rn as a tracer to evaluate model transport processes, not
to mention that 222Rn emission and distributions directly affect the
production of its progeny 210Pb, a useful tracer for testing aerosol
transport and wet deposition (Liu et al., 2001; Considine et al., 2005).
GEOS-Chem is driven by assimilated meteorological datasets archived from
the Goddard Earth Observing System (GEOS) of the NASA Global Modeling and
Assimilation Office (GMAO). Changes in the model dynamics often occur as the
GEOS model evolves, which in turn affect the characteristics of transport of
chemical species in GEOS-Chem. In an evaluation with satellite-observed
carbon monoxide in the upper troposphere and lower stratosphere (UTLS),
Liu et al. (2013)
reported that less carbon monoxide was lofted to the UTLS when GEOS-Chem was
driven by the GEOS-5 assimilated data due to insufficient vertical transport
compared with GEOS-4. Downward transport from the stratosphere to the troposphere
was previously found to be substantially overestimated in CTMs driven by
GEOS-1 compared with GEOS-4
(Liu et al., 2016). In a
similar manner, evaluation of 222Rn simulations with observations will
help characterize convective transport and its uncertainty in the GEOS
series.
In this paper, we assess and improve the simulation of 222Rn as a model
utility to test convective transport in GEOS-Chem. We incorporate recently published global 222Rn emission scenarios into the
model. We conduct
model simulations with varying emission configurations and provide insights
into potential biases in regional and seasonal emissions through evaluations
against observed surface 222Rn concentrations and vertical profiles. We
also present the changes in simulated 222Rn vertical distributions as
the driving meteorology switches between the Modern-Era Retrospective
analysis for Research and Applications (MERRA) and GEOS Forward Processing
(GEOS-FP), with a specific focus on the role of convection.
The rest of this paper is organized as follows. Section 2 describes the
GEOS-Chem model, four 222Rn emission scenarios, model simulations, and
observational datasets used in this work. Section 3 evaluates the four
different 222Rn emission scenarios by comparing simulated 222Rn
with surface measurements. Section 4 discusses potentially underestimated
222Rn emissions in Asia. Section 5 examines simulated surface
222Rn seasonality at selected sites. Section 6 assesses convective
transport in the model and compares the role of convective transport in
MERRA and GEOS-FP in the 222Rn vertical distribution.
Model and dataGEOS-Chem
GEOS-Chem (http://www.geos-chem.org, last access: 1 August 2020) is a global 3-D CTM of atmospheric
composition with aerosol–chemistry interactions in both the troposphere and
stratosphere, driven by GEOS assimilated meteorological fields from the NASA
GMAO (e.g.,
Bey et al., 2001; Park et al., 2004; Eastham et al., 2014). The model uses
a flux-form semi-Lagrangian finite volume scheme, known as TPCORE, to
calculate advection (Lin and Rood, 1996).
The scheme uses the monotonic piecewise parabolic method under convergence
conditions and a semi-Lagrangian method otherwise. Convective transport is
calculated using archived convective mass fluxes
(Wu et al., 2007).
Boundary-layer mixing is based on the non-local scheme implemented by
Lin and McElroy (2010). In this study,
we use two different GEOS products (MERRA and GEOS-FP) to drive the model
simulations. MERRA is a 30-year reanalysis product based on GEOS-5.2.0
(Rienecker et al., 2011). Its native resolution is 0.667∘
longitude by 0.5∘ latitude, with 72 vertical layers from the
surface up to 80 km. GEOS-FP is the current operational product of GEOS-5.7
(and after) using an analysis developed jointly with NOAA's National Centers
for Environmental Prediction (NCEP). It has a native resolution of
0.3125∘ longitude by 0.25∘ latitude, with the same
vertical grids as MERRA. Both the MERRA and GEOS-FP fields are regridded to
the resolution of 2.5∘ longitude by 2∘ latitude (with
vertical layers reduced to 47 levels) to drive GEOS-Chem simulations in this
study. The meteorological archives have temporal resolutions of 3 h for 3-D
fields and 1 h for 2-D fields. MERRA and GEOS-FP use similar model schemes
for fundamental dynamical and physical processes. They both use the modified
Relaxed Arakawa–Schubert scheme for convection
(Moorthi and Suarez, 1992) and a combined
turbulence parameterization based on Lock et al. (2000) and
Louis et al. (1981). Compared with MERRA, GEOS-FP made a few
adjustments including, but not limited to, increasing re-evaporation in
precipitation and adjusting the balance between local and non-local
turbulent diffusion, with the former resulting in a considerable increase in
water vapor in the tropical troposphere (Molod et
al., 2012). MERRA-2, which is based on a newer version of GEOS-5 and shows
improved climate over MERRA (Molod et al., 2015), is not used here since it
was not ready to drive our model when this study was started.
GEOS-Chem (v11-01f;
http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_v11-01, last access: 1 August 2020) includes a radionuclide (222Rn-210Pb-7Be) simulation
option, which runs independently from the full oxidant–aerosol chemistry
simulation. The radionuclide tracers have been used to evaluate chemical
transport (Jacob et al., 1997; Yu et al., 2018) and wet deposition processes
(Liu et al., 2001, 2004) in the
model. The simulation of 222Rn includes emissions, transport
(advection, convection, boundary-layer mixing), and radioactive decay. Wet
and dry deposition of 222Rn are neglected in the model.
222Rn emission scenarios
The standard version of GEOS-Chem uses the 222Rn emission scenario of
Jacob et al. (1997) (hereafter referred to as JA97). The World Climate
Research Program (WCRP) Cambridge Workshop of 1995 (Rasch et
al., 2000) previously used JA97 to compare 210Pb deposition processes
in multiple atmospheric models. JA97 was developed using the estimated
global mean 222Rn fluxes of Turekian et al. (1977) and
only considered emission variations for a few broad latitude bands. The
222Rn emission fluxes in JA97 are uniformly set to be 1 atom cm-2 s-1
over land between 60∘ N–60∘ S, 0.005 atom cm-2 s-1
between 60–70∘ N and 60–70∘ S, and zero poleward of 70∘ N or 70∘ S. Emission fluxes
over lakes and oceans are set to 0.005 atom cm-2 s-1. Emissions are reduced
by a factor of 3 when surface temperature is below 0 ∘C on account
of the depressed exhalation of 222Rn under freezing conditions. Such a
temperature-dependent reduction can lead to underestimated emissions in
winter because soils may not be totally frozen when the temperature falls below
0 ∘C for only a short period of time. The overall uncertainty of
the JA97 emission scenario was estimated to be within 25 % globally
(Jacob et al., 1997). Since the emission fluxes in
JA97 are fairly uniform over land area, this simplistic emission scenario
can be used to discern continental influence on air masses in global models
and assess the effect of any changes in the model representation of
convective mixing (Balkanski et al., 1992; Jacob et al., 1997).
A few 222Rn emission scenarios were published after
Jacob et al. (1997). Conen
and Robertson (2002) proposed a 222Rn emission scenario having a
uniform 222Rn flux of 1 atom cm-2 s-1 from the continental surface in
the Southern Hemisphere and tropics and a linear decreasing trend from 1 atom cm-2s-1 at 30∘ N to 0.2 atom cm-2 s-1 at 70∘ N.
This decreasing trend towards high latitudes was supported by experimental
results showing a decrease of 222Rn fluxes in the local soil with
a higher water table and organic portion. This proposed latitudinal gradient
was found to be in a good agreement with an estimated value based on
multi-year observations at a few Asian sites (Williams et
al., 2009). However, this emission scenario is not used in this work because
it does not include regional variations other than the linear latitudinal
gradient.
Schery and Wasiolek (1998, hereafter SW98) published the first global
222Rn emission inventory that included detailed regional and seasonal
variations on a monthly basis (at 1∘ longitude by 1∘
latitude resolution). The emission flux in SW98 is formulated by using a
theoretical diffusion model of porous soil with controlling factors of soil
radium content, soil moisture, and surface temperature. The estimated annual
global average 222Rn emission flux was 1.63±0.43 atom cm-2 s-1,
higher than the widely used JA97 constant value of 1 atom cm-2 s-1. Global
222Rn emissions in SW98 exhibited regional variations of a factor of 3
and seasonal variations of a factor of 2. The dominant factor in determining
the regional variations in 222Rn fluxes was found to be the soil radium
concentrations according to Schery and Wasiolek (1998). Emission fluxes in
the United States and China feature more detailed regional variations because soil
radium concentrations in these countries were incorporated. However, it was
suggested that SW98 overestimated the global average 222Rn flux
(Koch
et al., 2006; Zhang et al., 2011). When using the SW98 emissions in a global
model simulation of 210Pb, Koch et al. (2006) found it necessary to
reduce the emissions by half to improve the excessive 210Pb
concentrations in their model. In this study, we reduce the emission fluxes
of SW98 globally by a factor of 1.6, as recommended by Zhang et al. (2011).
Zhang et al. (2011, hereafter ZK11) compiled a new global 222Rn
emission inventory based on a combination of SW98 (with a global reduction
factor of 1.6) and recently published 222Rn flux measurements in Europe
and the United States (Szegvary et al., 2007), China
(Zhuo et al., 2008), Australia (Griffiths et
al., 2010), and oceanic regions (Schery and Huang, 2004). In
ZK11, 222Rn emissions in Europe were derived from a demonstrated linear
relationship between the terrestrial gamma dose rate and 222Rn emissions
(Szegvary et al., 2007). The relationship allows for a
convenient calculation of regional 222Rn emissions for places where
automatic measurements of gamma dose rate have been established, e.g., in
Europe (Szegvary et al., 2009).
This 222Rn emission inventory for Europe has recently been updated
(Karstens et al., 2015; López-Coto et al., 2013)
with further detailed information on soil and surface roughness and minor
modifications about handling 222Rn transport in porous media. A
high-resolution (25 km × 25 km) 222Rn emission map for China
was included in ZK11 based on work by Zhuo et al. (2008),
who estimated the nationwide emissions according to measurements of radium
content in surface soil at 1099 sites in China. The oceanic emission flux
used was 0.00182 atom cm-2 s-1, derived by Schery and Huang (2004) with a
gas transfer model, significantly lower than typical 222Rn emissions
over land. For model surface grid boxes containing both land and water
surfaces, we sum 222Rn emission fluxes weighted by their respective
areas of land or water.
Global 222Rn emission scenarios used in this work.
ScenarioReferenceDescriptionJA97Jacob et al. (1997)Emission fluxes are 1 atom cm-2 s-1 over land between 60–60∘ S, 0.005 atom cm-2 s-1 between 60–70∘ N and 60–70∘ S, zero poleward of 70∘ N or 70∘ S, and 0.005 atom cm-2 s-1 over lakes and oceans. Emissions are reduced by a factor of 3 when surface temperature is below 0 ∘C.SW98Schery and Wasiolek (1998)Emission fluxes are formulated by using a theoretical diffusion model of porous soil with controlling factors of soil radium content, soil moisture, and surface temperature. Emission fluxes in SW98 were found to be overestimated and are reduced by a factor of 1.6 globally in this work (Koch et al., 2006; Zhang et al., 2011)ZK11Zhang et al. (2011)Based on SW98, ZK11 updated emission fluxes over Europe, the United States, China, Australia, and oceanic regions according to more recent measurements.ZKCThis workZKC increases emission fluxes in the geographic territory of China by a factor of 1.2 upon ZK11 and retrogresses to SW98 over the United States.
In this study, we modify ZK11 to a customized 222Rn emission scenario
(hereafter referred to as ZKC) and constrain the inventory with observations
of surface 222Rn concentrations. This customized emission scenario
adopts ZK11 for most areas except for North America, where the SW98 emission
fluxes are used with a reduction factor of 1.6, following previous model
studies
(Koch
et al., 2006; Zhang et al., 2011). We also increase the emission over China
by a factor of 1.2 all year round due to potentially underestimated
222Rn emission there, which will be discussed in detail in Sect. 3.2.
The emission enhancement factor is only tentative due to very few surface
222Rn measurements available in western China and a lack of seasonality
in the measurements. Since ZK11 has been tested with satisfactory agreement
between modeled and observed surface concentrations in Europe (Zhang et al.,
2011), the updates for emission fluxes in Europe by López-Coto et al. (2013) and Karstens et al. (2015) are not included. The largest terrestrial
spatial variation of 222Rn emission rates in ZKC is a factor of 10. A
description of the four emission scenarios is given in Table 1.
Global annual mean surface 222Rn emission fluxes (atom cm-2 s-1) of four emission scenarios used in this study. (a) JA97, the default emission scenario in the standard version of GEOS-Chem (Jacob et al., 1997); (b) SW98, the first global 222Rn emission inventory with regional variability based on a soil emission model (Schery and Wasiolek, 1998); (c) ZK11, a recently published global 222Rn emission inventory combining SW98 and recent measurements of 222Rn fluxes (Zhang et al., 2011); and (d) ZKC, which is ZK11 with customized adjustments to better match observations (this work).
Figure 1 shows the global annual mean 222Rn emission fluxes of the four
emission scenarios described above. Compared with the standard GEOS-Chem
222Rn emission scenario (JA97, Fig. 1a), the other three show evident
spatial variations of varying extents due to incorporation of observations
and estimates from soil exhalation models. The estimated global total
222Rn emissions for JA97, SW98, ZK11, and ZKC are 1.94, 2.41, 2.11, and 2.22 GCi yr-1 (where GCi denotes the unit gigacurie), respectively. Since there is no
consensus on the global total 222Rn emission, we do not normalize the
total emission amount for each scenario. Instead, the overall evaluation of
the emission scenarios is based on comparisons with surface 222Rn
observations. It is clearly shown in Fig. 1 that the three later 222Rn
emission scenarios have substantial enhancements of 222Rn emission
fluxes in North America and East Asia. SW98 exhibits more intense 222Rn
emissions in North America, which have been adopted in ZKC. In the northern
polar region, SW98 presents much higher 222Rn emissions over Siberia
extending to higher latitudes. JA97 is overly simplified and has nearly no
emissions over Siberia due to temperature-dependent reduction in the cold
high-latitude regions. The ZK11 emissions in Siberia stay between those of
JA97 and SW98, with somewhat higher emissions in eastern Siberia. ZK11
has much higher 222Rn emissions in China, which are further scaled up
by a factor of 1.2 in ZKC.
Same as Fig. 1 but for January.
Same as Fig. 1 but for July.
Seasonal variations of 222Rn emissions are considered in all four
scenarios but with different approaches. In JA97, 222Rn emissions are
reduced by a factor of 3 when surface temperature in the driving
meteorological fields falls below 0 ∘C, thus resulting in
seasonal variations of 222Rn emissions in high-latitude regions. In the
other emission scenarios, the monthly varying 222Rn emission fluxes in
each model bottom layer are prescribed based on observed and assumed soil
parameters (see SW98) and do not change from year to year. Figures 2 and 3
compare monthly mean 222Rn emissions for January and July in the
emission inventories. 222Rn emissions are generally the lowest in
January because of the inhibition of exhalation as a result of ice cover and
high moisture content. All emission scenarios exhibit increased global
222Rn emissions by a factor of 1.2 to 1.4 in July compared with January
due to enhanced emissions over the Northern Hemisphere continents. The
summer–winter changes of local emissions are mostly within a factor of 2.
The possible underestimation of emissions for surface temperatures under 0 ∘C is revised in the later emission scenarios, leading to
increased wintertime emissions in central and East Asia, North America,
and southern Europe (Fig. 2a vs. d). The affected regions extend to lower
latitudes in East Asia and North America compared with relatively warmer
Europe.
Compared to JA97, significant emission increases occur in middle–low latitudes
where land is covered by desert or mountainous texture in the later emission
scenarios, e.g., the western united States and western China. Rocky and desert land types
are more favorable for 222Rn emission compared with soil. Previous
related literature and analyses support these emission modifications based
on evaluations against existing surface observations. We speculate that some
degree of emission increase would be reasonable in the Middle East and North
Africa, where land is mostly covered by desert. Emissions in these areas in
ZK11 and ZKC are adapted from SW98, which uses a world average surface
radium content to calculate 222Rn emission. No observations of surface
radium content or 222Rn concentrations exist for evaluating speculated
emission modifications, but future changes are possible when measurements
become available in these areas.
Model simulations and observational data
We simulated 222Rn with the model driven by MERRA using the four
emission scenarios. The preferred emission scenario was then identified
based on a comparison of simulated and observed surface 222Rn
concentrations and seasonality. To characterize convective transport in the
MERRA and GEOS-FP products, we also conducted model sensitivity experiments
for which convective transport was turned off. All simulations were
conducted for the year of 2013 with a 12-month spin-up, which was
initialized with a climatological restart file from previous model
simulations. Table 2 lists all the model experiments and their
configurations.
Configurations of GEOS-Chem simulations (v11-01f, 2∘× 2.5∘) used in this work.
Simulation222Rn emissionDrivingConvectionmeteorologyA1JA97 (Jacob et al., 1997)MERRAonA2SW98 (Schery and Wasiolek, 1998)MERRAonA3ZK11 (Zhang et al., 2011)MERRAonA4ZKC (this work)MERRAonB1JA97 (Jacob et al., 1997)GEOS-FPonA1-ncJA97 (Jacob et al., 1997)MERRAoffB1-ncJA97 (Jacob et al., 1997)GEOS-FPoff
Locations of surface 222Rn measurement sites. Sites in four
distinctive regions are color-coded: Europe (blue), Asia (purple), North
America (red), and remote regions (black). Refer to Table 2 of Zhang et al. (2011) for more details.
We evaluate the 2013 simulations against long-term monthly or annual
222Rn observations. We used the observed surface 222Rn
concentration dataset compiled by Zhang et al. (2011), who evaluated the
ZK11 emission scenario in their model. Figure 4 shows the locations of 51
surface 222Rn measurement sites. The sites are concentrated in Europe,
North America, and East Asia. Fewer sites (11) are located in the Southern
Hemisphere. No observations in boreal Canada and Siberia are available. The
few inland sites in China only reported annual means based on measurements
of 1–2 years (Jin et al., 1998). The 222Rn observations were made in
multiple years, and we treat the calculated multi-year monthly means as
climatological. We also include longer period observations at Mauna Loa
(2004–2010; Chambers et al., 2016c) and Gosan (2001–2010; Chambers et al.,
2016b) stations in addition to those compiled by Zhang et al. (2011) in our
analyses below. Considering the monthly climatological surface 222Rn
observations used in the comparisons, simulations driven by MERRA for
alternative years do not change the conclusions of this study.
To examine simulated convective transport characteristics, we compare model
results with four observational datasets of 222Rn vertical profiles
(Liu et al., 1984; Zaucker et al., 1996; Kritz et al., 1998; Williams et
al., 2011). The scarcity of 222Rn airborne measurements is partly due
to the fact that the measurement requires an extraction and counting
facility nearby in order to minimize decay and that the process of radon
extraction is labor-intensive (Williams et al., 2011).
Liu et al. (1984) compiled an extensive
dataset of 222Rn profiles for different seasons based on airborne
measurements made in the 1950s–1970s. The summertime average profile was
calculated from 23 sites in the United States, Ukraine, and central Asia and mainly
represents the summertime 222Rn vertical distribution over the Northern
Hemisphere midlatitude continental regions. Zaucker et al. (1996) reported nine 222Rn profiles measured during flights from the east
coast of Canada (Nova Scotia and New Brunswick) to the North Atlantic as
part of the North Atlantic Regional Experiment (NARE; August 1993).
Kritz et al. (1998) measured 222Rn vertical profiles
at Moffett Field (37.4∘ N, 122.0∘ W), a coastal site
in California, United States, during April to August in 1994. The Moffett profile
represents summertime 222Rn vertical distribution over an offshore
region. Williams et al. (2011) made aircraft measurements of 222Rn
profiles up to 3.5 km altitude at Goulburn (34.8∘ S,
149.7∘ E), an inland rural site in New South Wales, Australia,
during May 2006–2008 and January 2007 (Williams
et al., 2011).
Model results and evaluation with surface observationsModel surface 222Rn
Figure 5a–b show the global surface 222Rn concentrations for January
and July 2013, as simulated by GEOS-Chem with the JA97 emission scenario
(simulation A1, Table 2). Surface 222Rn concentrations are much higher
over land at low latitudes and midlatitudes compared with marine areas and
high latitudes. Typical surface 222Rn concentrations over land range
from a few hundreds to about 1.0 × 104 mBq SCM-1. Surface
concentrations drop sharply from land to oceanic regions due to the short
lifetime of 222Rn, with values over the oceans ∼ 2–3
magnitudes lower and ranging from tens to a few hundreds of mBq SCM-1. The
model simulates a noticeable outflow of 222Rn at surface level from the
west coast of Africa to South America in January. Surface 222Rn
concentrations are higher overall in winter due to shallower boundary layers
than in summer (Fig. 5a vs. b). For example, concentrations in Europe,
central and East Asia, and North Africa are lower by a few thousands of mBq SCM-1
in July (summer), while concentrations in South Africa, Argentina, and
Australia are higher by similar magnitudes in July (winter). The contrasting
seasonality of surface 222Rn concentrations (high in winter) compared
to emission fluxes (high in summer) suggests that the accumulation effect in
shallower boundary layers (weakened vertical transport and mixing; see Fig. S1 in the Supplement) dominates the seasonal changes in emission when it comes to determining
the seasonality of surface 222Rn concentrations.
Simulated monthly surface 222Rn concentrations (mBq/SCM) for
(a) January 2013 and (b) July 2013 with the JA97 emission scenario (A1; see Table 2). Panels (c)–(h) are the same as panels (a) and (b) but show the changes in
surface 222Rn concentrations when SW98 (A2), ZK11 (A3), and ZKC (A4)
emissions are used in the model, respectively.
Figure 5c–h show the changes in simulated surface 222Rn concentrations
when the SW98, ZK11, and ZKC emissions are used (simulation A2–A4, Table 2),
relative to the standard simulation with the JA97 emissions. All three
alternative 222Rn emission scenarios lead to remarkable increases in
surface concentrations in midlatitude and high-latitude regions of North America
and Asia. With SW98 (simulation A2, Fig. 5c), wintertime surface 222Rn
concentrations increased from very low levels (< 1000 mBq SCM-1) to
about 1.0 × 104 mBq SCM-1 in many high-latitude regions,
including the northwestern United States, Alaska, and northern Canada, as well as in the
continental areas extending from eastern Europe through Siberia to the
Bering Strait. These large increases are mainly due to the zero emission
flux rate prescribed for high latitudes (> 60∘ N) in
JA97, which is replaced in SW98 by fluxes from 0.3 to 0.6 atom cm-2 s-1(Figs. 2b and 3b). As shown later, this characteristic in JA97 overly simplifies
222Rn emission variations and causes underestimation of surface
222Rn concentrations in high-latitude regions in winter. Accumulation
of 222Rn in the shallow winter boundary layer also contributes to and
enhances the differences in surface 222Rn concentration caused by
increased emissions. In the ZK11 simulation, similar enhancements of surface
222Rn appear in North America, China, and the far east of Siberia (Fig. 5e, f), but the overall magnitudes of enhancement are smaller than those
with SW98. The largest enhancements in Asia shift to the east and are seen
in eastern Siberia rather than the whole of boreal Siberia. ZK11 incorporates
recent 222Rn flux measurements in Asia (K. Zhang et al., 2008) and shows
some smaller changes from those of JA97 in Siberia. Since ZKC and SW98 share
the same emissions for North America, the surface concentration changes are
mostly identical between the two. For the same reason, the ZKC and ZK11
results look similar in Asia, except that the surface concentrations over
China are more enhanced due to the upscaling in ZKC. In July (Fig. 5d, f, h),
the changes in surface 222Rn concentrations are much less significant
for all emission scenarios. This also reflects the strong effects of summer
boundary-layer ventilation, which largely compensates for the differences
caused by the seasonal emission changes.
Evaluation of emission scenarios with surface observations
Following Zhang et al. (2011), we evaluate the 222Rn emission scenarios
by comparing model results with surface observations of 222Rn
concentrations. We conduct the comparisons in the form of scatter plots for
Europe (EU), Asia (AS), North America (NA), and over the globe (ALL),
respectively (Fig. 6). For each observed monthly or annual mean, model
output was sampled in the grid cell corresponding to the physical location
and elevation of each site and then averaged for the corresponding
observation time period. Also shown in Fig. 6 are the reduced major axis
linear correlation coefficients (R;
Hirsch and Gilroy, 1984) and the
percentages (P) of the data points lying within a range of a factor of 2
(dashed lines).
Comparisons between simulated and observed monthly surface
222Rn concentrations (mBq/SCM) over the continents of Europe (EU, first
row), Asia (AS, second row), and North America (NA, third row) and over the
globe (ALL, last row), respectively. The four columns correspond to
simulations (A1–A4) with the four emission inventories (JA97, SW98, ZK11,
and ZKC; see Table 2). Dashed lines indicate the range within a factor of
2 of the 1-to-1 line. P is the percentage of samples within this range. R in the legends is the two-sided linear regression correlation coefficient. The lines of best fit are calculated using the reduced major axis method (Hirsch and Gilroy, 1984).
Europe is the continent where emission fluxes and transport of 222Rn
have been studied most extensively. The measurements are more widely and
evenly distributed across the continent (Fig. 4). The JA97 simulation (A1,
Table 2) shows moderate agreement with observations (P= 66.5 %) bearing
some large underestimates (Fig. 6a). The SW98 simulation has the lowest P
value of 61.9 % (Fig. 6b) due to a large number of points with high
biases. ZK11 and ZKC use the same 222Rn emissions in Europe, and the P
values are close (80.3 % and 80.7 % in Fig. 6c and d,
respectively). The better agreement when using ZK11 and ZKC substantiates
the high-resolution 222Rn emission estimates derived from gamma dose
rates in Europe (Szegvary et al., 2009).
Schmithüsen et al. (2017)
compared measured 222Rn concentrations across the European sites in
terms of different instruments and measurement systems and provided
suggested scaling factors for each site. Here, the same evaluation for the
emission scenarios with the scaling factors is given in Fig. S2. There are
only slight changes in the P values for all regional groups, and the same
rank of the four emission scenarios remains.
All simulations exhibit some degree of underestimation in Asia (Fig. 6e–h).
Monthly mean observations are available for 7 of the 12 Asian sites;
otherwise only annual means are available. Consequently, data points are
sparse in Fig. 6e–h. The JA97 simulation shows poor agreement for Asia (P= 46.3 %, Fig. 6e). Agreement for the others is better but still
deficient, with P values of 64.2 %, 67.2 %, and 68.7 % for SW98, ZK11,
and ZKC, respectively (Fig. 6f–h). The few underestimated data points in
Fig. 6g and h are observed annual means from the inland Chinese sites. The
observations in China were taken between 1 and 1.5 m above ground according
to Jin et al. (1998). The model surface layer concentrations usually
represent the averages in the model bottom layer (∼ 100 m
high) and thus may be literally lower than the observations due to the
steep concentration gradients near the surface, especially during nighttime
(Chambers et al., 2011). On the other hand, there are possible low biases in
the 222Rn concentrations derived from 222Rn progeny measurements
(Schmithüsen et al., 2017; Grossi et al., 2020), lessening the above
model underestimate due to large near-surface vertical gradients. These
biases differ on a case-by-case basis and are difficult to quantify. With
upscaled emission in ZKC, the improvement compared with ZK11 is minor. To
better match the Asian observations, we tentatively conducted additional
model simulations in which the Asian 222Rn emission fluxes are scaled
up by a factor of 1.5 or 1.7 (instead of 1.2 in the recommended ZKC). The P
values from those simulations are larger with some previously underestimated
data moving into the factor-of-2 range; upscaling by a factor of 1.5 would
increase the P value to above 70 %, but the simulated total 210Pb
deposition fluxes at midlatitudes would be substantially overestimated
(Zhang et al., 2021). As will be discussed in Sect. 4, a few studies reported
unusually high surface concentrations and large emission fluxes at
individual sites in Asia; the evidence in these studies endorsed a higher upscaling
factor, which would reduce the model underestimates of surface
concentrations. However, without knowing the distributions and varying
extents of emission biases in Asia, applying a higher and uniform scaling
factor to the whole region may worsen the global simulation of 210Pb
deposition. The few annual means that lead to the low P values may not be as
representative as the monthly data and can be biased. Therefore, we use a
tentative scaling factor of 1.2 for emission fluxes in China (i.e., ZKC) and
expect future improvements when more observations of 222Rn emission and
surface concentrations become available.
All simulations reproduced the observed surface 222Rn concentrations in
North America well (Fig. 6i–l). SW98 (Fig. 6j) and ZKC (Fig. 6l) share
identical 222Rn emissions over North America, and simulations with both
emission scenarios show excellent agreement with the observations (P∼ 90 %, Fig. 6j, l). This suggests that SW98 is an adequate
option for 222Rn emissions in North America. Interestingly, ZKC leads
to slightly better agreement compared with SW98, although identical
emissions were used for North America. A few overestimated data points in
the simulation with SW98 are better simulated with ZKC at the United States west
coast sites, as a result of the large reduction in emissions over the upwind
Siberia regions (Fig. 1d). Despite the good agreement between model results
and observations, the evaluation is limited to the western and eastern
coastal regions of the United States. Data from Africa, the central United States, and Canada
are currently lacking and would otherwise improve the model evaluation,
especially over North America.
Figure 6m–p show the overall evaluation of the model results against
measurements at all 51 surface sites over the globe. Both ZK11 and ZKC
simulations show better agreement with observations (P= 76.9 % and
78.4 %; Fig. 6o, p), suggesting that ZK11 and ZKC are potentially better
choices for replacing the JA97 emission scenario in the standard version of
GEOS-Chem, although with its provisional effort to address high Asian
emissions, ZKC is a step ahead of ZK11. The large biases of a few points
outside the factor-of-2 range are from the Antarctic sites. None of the
simulations can reasonably represent observations in Antarctica, which can
be attributed to emission that is not well characterized
(Chambers et al., 2018) and will be discussed later.
If the two Antarctic sites (with model low biases in the lower left corner
of Fig. 6o, p) were excluded, the P values for ZK11 and ZKC would increase
to over 80 %.
Excessive 222Rn emissions in East Asia
Unusually high 222Rn emissions have been observed over mainland Asia
(Iida et al., 2000; Yamanishi et al., 1991) and downwind
regions (e.g., Korea; Zahorowski et al., 2005). These
high 222Rn emissions, not well represented in JA97-like emission
scenarios, were likely responsible for the failure of CTMs in capturing the
222Rn concentrations observed in East Asia
(Jacob et al., 1997). In particular, 222Rn
emissions over China have been underestimated at inland cites (Zhang et al.,
2011). China and India have been identified as regions of high 222Rn
emissions from soil. It was suspected that this is partially due to high
soil content of radium (Schery, 2004; Zhuo et al., 2008). Schery (2004)
presented global measurements of radium content in soil, which clearly
indicated that the radium concentrations are higher by about a factor of 3
in the southeastern compared to the northwestern China. Consistent with this,
Zahorowski et al. (2005) found that surface 222Rn concentrations were
roughly 3 times higher at Hok Tsui (Hong Kong) during winter compared to
Gosan, where fetch is from northern China and Mongolia. Zhuo et al. (2008)
provided an estimated area-weighted annual average 222Rn emission of
29.7 mBq m-2 s-1 (∼ 1.41 atom m-2 s-1) in China. Based on
3-year wintertime 222Rn observations at Sado Island, Japan, and
associated trajectory analyses, Williams et al. (2009)
suggested that emission fluxes can be 1.75 times higher in the lower
latitude bands over the Asian continent compared to higher latitudes. In an
inverse modeling of Asian 222Rn emissions, Hirao et
al. (2010) showed an area-weighted average 222Rn emission of 33.0 mBq m-2 s-1
(∼ 1.57 atom m-2 s-1) in Asia with the highest emissions
found in central and southeastern Asia. These values are considered much
higher than typical 222Rn emission known for Europe, where Szegvary et
al. (2009) suggested half of the continent has emissions ranging from 8.33
to 14.6 mBq m-2 s-1 (0.40 to 0.70 atom m-2 s-1).
Hirao et al. (2010) also found that, to better match
surface observations at Hachijo Island, a volcanic island about 287 km south of Tokyo in the Philippine Sea, the emissions over East
Asia would need to be increased by a factor of 1.69.
It is likely that the high 222Rn emissions in Asia are poorly estimated
because of the diverse climate and geographic textures formed on the largest
continent of the earth. The southern part of China is known to be covered
with soils containing higher radium concentrations than the global average
(Schery and Wasiolek, 1998). Central Asia is dry and sparsely covered with
soils, which could facilitate 222Rn emanation. The mountainous surface
in southeastern China could also be conducive to high 222Rn emissions.
The 222Rn exhalation model developed by Hirao et al. (2010) took into consideration 222Rn emission enhancements caused by
rough surfaces but still underestimated 222Rn concentrations in East
Asia. Active crust movements along the east coast of Asia can cause more
exposure of radium and extra 222Rn emissions. Intense human activities
may also contribute to excessive 222Rn emissions in Asia.
Moore et al. (1976) pointed out that phosphate ores
contain high concentration of 238U (precursor of radium) and are widely
used as phosphate fertilizers in the populous East Asia region. Due to such
complexities and uncertainties, most of the 222Rn exhalation models are
not well validated in Asia, and a lack of 222Rn measurements in central
and western Asia adds to the difficulty.
(a) Comparison of seasonal total 210Pb deposition fluxes (mBq cm-2 yr-1) at Shanghai (32.1∘ N, 123.4∘ E) between five model simulations (see Table 2) and observations (Du et al., 2015). (b) Correlations between simulated and observed annual mean 210Pb deposition fluxes at nine surface sites in North America (Du et al., 2015). (c) Same as panel (b) but for nine Asian sites. The dashed line is the 1-to-1 line. Colored lines are linear regression lines for the five model simulations shown in the legends. The reduced major axis regression slopes (S) and correlation coefficients (R) are given in the legends.
An alternative way to verify 222Rn emissions is to evaluate the
deposition fluxes of its long-lived decay daughter, 210Pb. Since
surface deposition is the primary sink of 210Pb aerosols, global
210Pb deposition fluxes should be balanced by 210Pb production
or 222Rn emission fluxes (Considine et al., 2005). Regional total
210Pb deposition fluxes, however, can be affected by transport into and
out of the region. Nevertheless, comparisons between simulated and observed
210Pb deposition fluxes at multiple locations in Asia offer a test of
underestimated 222Rn emissions. Figure 7a compares model results with
observed 210Pb total (dry and wet) deposition in Shanghai for each
season averaged over an 8-year period (Du et al., 2015). All model
simulations, including the simulation with upscaled emission in China (ZKC),
underestimate the total deposition in all seasons. Enhanced 222Rn
emissions in ZKC improve the simulated 210Pb deposition to a limited
extent in all seasons and more favorably in winter. We then calculate the
correlations between simulated and observed annual mean 210Pb
deposition fluxes at the sites in North America (nine sites) and Asia (nine
sites; Du et al., 2015). Some studied Asian sites are located in northern
and inland China. Details about these sites can be found in
Du et al. (2015). The reduced major axis regression
slopes for North American sites are closer to 1 (Fig. 7b), indicating a
generally well simulated life cycle from 222Rn emission to 210Pb
deposition. By contrast, the slopes for Asian sites are much lower than 1.
This large magnitude of model underestimation in 210Pb deposition
fluxes can only be attributed to low 222Rn emissions in Asia. Much
existing evidence suggests using a larger scaling factor, but as mentioned
earlier, we choose to use a moderate scaling factor of only 1.2 for China to
avoid large overestimates of total 210Pb deposition fluxes over the
rest of the Northern Hemisphere.
Seasonality in surface 222Rn concentrations
The seasonality in surface 222Rn concentrations is mainly affected by
three factors: (1) the variability in 222Rn emission flux rate due to
seasonal changes in soil moisture, diffusivity, depth of the water table,
and snow and ice coverage; (2) the vertical mixing processes (i.e.,
boundary-layer mixing and convection); and (3) advective transport of
222Rn-rich or 222Rn-poor air masses. The roles of these factors may vary by
location. Here, we examine the seasonal variations of surface 222Rn
concentrations at selected surface sites in Europe, Asia, and North America
and discuss these impacting factors. The selection of surface sites is
mainly based on the availability of multiple-year measurements, with
consideration of special geographic locations indicative of regional
transport patterns.
Europe
Observations in Europe were mostly obtained in Finland,
Germany, France, and Italy, with about half of the sites in Finland. Figure 8a–c show the comparisons of model results with monthly mean observations at
three Finland sites (Kevo, Pallas, and Joensuu). At these high-latitude
sites, the highest monthly concentration does not exceed 4000 mBq SCM-1,but the
seasonal variations are large, with the observed wintertime highs being up
to twice the summertime lows. Such seasonal variation is mainly due to
a shallower boundary layer and less convection in winter because the changes
in 222Rn emissions are minor due to low temperature all year round (see
Figs. 2 and 3). Szegvary et al. (2009) suggested that the 222Rn
emissions in northern Europe are generally lower than the commonly used
value of 1 atom cm-2 s-1. The soil water content is high because of the
long snowy winter and short summer there. The content of radium is also
found to be lower than average in the quaternary sand deposits. The ZK11 and
ZKC emission scenarios, which adopted 222Rn emission fluxes derived
from measured gamma radiation (Szegvary et al., 2009), are clearly the
better options and result in better simulated seasonal variations
(frequently overlapped purple and red lines in Fig. 8a–c). The SW98
emissions lead to much higher 222Rn concentrations compared with the
observations, whereas JA97 tends to underestimate the emissions and results
in lower concentrations.
Comparison between observed 222Rn climatological monthly
means (black lines) and simulated monthly means in 2013 (colored lines) at
selected surface sites in Europe. Location and elevation of each site are
given above each panel. See Table 2 for the list of model simulations. Note
the small difference between the simulations with ZK11 and those with ZKC
because of identical 222Rn emission in Europe.
Figure 8d–f show model–observation comparisons at three sites in central
mainland Europe, i.e., Hohenpeissenberg (Germany), Freiburg (Germany), and
Gif-sur-Yvette (France). The observations generally show minimal surface
concentrations in spring and maximums in late fall. The highs appear earlier
with larger seasonal amplitudes compared with the Finland sites as a result
of the combined effects of seasonal changes in emission fluxes and vertical
transport. In general, the lowest 222Rn concentrations usually occur
during spring and summer when convection and boundary-layer mixing are most
active at inland surface sites (Wilkening, 1959; Lindeken,
1967). Higher wintertime concentrations at central European sites were also
likely attributed to slow transport and long residence time over land due to
air mass stagnation
(Chambers
et al., 2016a; Williams et al., 2016). At midlatitude sites, snow cover
suppresses 222Rn exhalation and reduces emission fluxes substantially
in winter; complete snowmelt and moist fluxes enhance 222Rn emissions in
summer (Reithmeier and Sausen, 2002). Since strong emissions in summer
partially compensate for the dilution effect of boundary-layer mixing and strong
convection, the lowest 222Rn concentrations are usually observed in the
springtime. All simulations capture the seasonal variations; ZK11 and ZKC
emission scenarios do not lead to obviously better results than JA97 and
SW98. It appears that a sharp increase in emission is missing from summer to
late fall as indicated by increased observations in June–August, suggesting
that further emission adjustments are needed for Europe in the model.
Szegvary et al. (2009) also suggested large 222Rn emissions over the
Iberian Peninsula and the northern Mediterranean coastal region due to a
wide coverage by dry soil and crystalline rocks. In a more recent study
using 222Rn as a tracer to classify atmospheric stability in Slovenia, an unusually large 222Rn exhalation flux from flysch and carbonate rocks
at an inland site was found to cause higher 222Rn concentrations in the
diurnal cycle compared to a coastal site where atmospheric synoptic
conditions were considered similar but land was more dominated by sea and
lake sediments (Kikaj et al., 2019).
Figure 8g shows the model–observation comparison for Mace Head, a coastal
site in western Europe (Ireland). Most observations are lower than 1000 mBq SCM-1,
with a weak seasonal variation. Simulations with JA97 and SW98 overestimate
the observations by a factor of > 2 on average, while such large
overestimates are only seen in February for ZK11 and ZKC. The coastal site
is usually moist and largely affected by oceanic air; it is therefore
characterized by relatively low 222Rn concentrations all year round. A
regional model simulation by Chevillard et al. (2002) with a
JA97-like, uniform 222Rn emission rate, showed similar overestimation
with much larger discrepancies from observations. The site is located
(53.3∘ N) very close to the cutoff latitude (60∘ N) in
JA97, at which zero emissions are assumed northwards. The comparisons in
Europe suggest that the fixed emission fluxes (with reductions under
freezing conditions) in JA97 can lead to overestimation in southern Europe and
underestimation in the north. A weaker latitudinal gradient towards the
north as shown by ZK11 and ZKC is much favored. The comparisons with
measurements applied with scaling factors suggested by Schmithüsen et
al. (2017) are given in Fig. S3, which only shows slight changes.
Asia
Observations of surface 222Rn concentrations in Asia,
e.g., southern China (Zahorowski et al., 2005), Japan
(Chambers et al., 2009; Iida et al., 2000), and India
(Debaje et al., 1996), are clearly affected by the Asian summer
monsoon, with maximum concentrations observed in winter and minimums in
summer (low-222Rn marine air brought by the monsoon). Figure 9 shows
the model–observation comparisons at five Asian sites (Beijing, Gosan,
Fuzhou, Hong Kong, and Bombay). Inland sites in China, where only annual
mean observations are available, are not included in this comparison. The
observations at Beijing show a moderate seasonal variation similar to the
midlatitude continental European sites, with a spring minimum and an autumn
maximum. The simulation with JA97 shows reasonable agreement with
observations at Beijing only in spring and summer but is significantly
biased low in late fall–early spring (November–March, Fig. 9a). The latter
is likely due to the temperature-dependent reduction of 222Rn emissions
in JA97, when surface temperature is below 0 ∘C. In reality, soil
may not be frozen when temperature remains below 0 ∘C for a short
period of time. At Gosan, an island site largely affected by the Asian monsoon
and emissions from the major Asian continent, observations show a strong
seasonal variation, with a winter maximum and a summer minimum. The large
winter low bias at Gosan with JA97 is likely also due to the assumed
dependency on surface temperature.
Same as Fig. 8 but for Asia. Note that the model results used in (c) Fuzhou and (d) Hong Kong are sampled at the grid boxes to the west of the ones where the sites are located to achieve a better agreement with the observations. See text for details.
At two coastal Chinese sites, Fuzhou and Hong Kong, the model results at the
corresponding grid boxes are much lower than the observations (Fig. S4). We
tried sampling the model results at adjacent grid boxes and found that those
for the grid box to the west are much more comparable to the observed (Fig. 9c and d). This suggests that the observations at both sites are
significantly affected by local 222Rn emissions. The 222Rn
observations show a minimum in summer, reflecting the intrusion of
low-222Rn marine air associated with the Asian summer monsoon. Although
the model successfully captures the observed seasonality, the simulation
with ZKC (with enhanced emissions in China) shows a much better agreement
compared to the large low bias in the simulation with JA97. On the other
hand, the simulations with ZK11 and ZKC capture the observations at Bombay,
India, well. These contrasting model performances suggest that 222Rn
emission fluxes in southeastern China need to be better quantified with flux
measurements at more surface sites.
North America
Figure 10 shows the model–observation comparisons at
four US continental sites. Similar to those midlatitude surface sites in
Europe and Asia, the observations at the US sites show seasonal lows in
spring and highs in fall or winter. The simulations with SW98 and ZKC
(identical emissions over North America) show much higher 222Rn
concentrations than those with JA97 and ZK11 over the United States. The seasonality
at Chester is well captured by using SW98 and ZKC. At Cincinnati, the model
performs slightly better with JA97 and ZK11, while the simulations with SW98
and ZKC overestimate the autumn peaks by nearly a factor of 2. SW98 and
ZKC lead to significant positive biases at Washington D.C., even though ZK11
commits negative biases of a similar magnitude. At Socorro, an elevated site
(1400 m a.s.l.) in the southern United States, all simulations do not convincingly
capture the seasonal variation (Fig. 10d). Socorro is located in the Rio
Grande Valley, where 222Rn emissions may have larger variations due to
surface textures and local meteorology (e.g., upslope air flows) that cannot
be resolved by the coarse-resolution model.
Same as Fig. 8 but for North America.
Other sites
Figure 11 shows the seasonal variations of surface
222Rn concentrations at eight sites in remote areas or the Southern
Hemisphere. Surface 222Rn concentrations at Bermuda show a late spring
to summer minimum (May–August) due to the strengthened Azores–Bermuda High
pressure system in summer which brings low-222Rn air from the central
and eastern North Atlantic (Fig. 11a). At Mauna Loa, observations are in a
low range of 75–150 mBq SCM-1 all year round, reflecting low 222Rn in
marine free tropospheric air (Fig. 11b). The seasonality is, however,
distinct, with a minimum in summer and a maximum in late winter and early spring when
efficient monsoonal transport of continental air occurs (Balkanski et al.,
1992; Zahorowski et al., 2005). At both remote sites, the model captures the
seasonality reasonably. The seasonal amplitudes in all simulations are
larger than observed, except with JA97. The simulation with JA97 better
captures the observed amplitude but substantially underestimates the
concentrations. It is challenging for a coarse-resolution global model (with
unresolved topography and grid-averaged local emissions) to accurately
simulate the low 222Rn concentrations at such a remote island.
Same as Fig. 8 but for remote sites.
Figure 11c–f show the 222Rn seasonality at three subtropical sites,
Chacaltaya (Bolivia), Rio de Janeiro (Brazil), and Cape Point (South Africa;
Botha et al., 2018), and one midlatitude site, Cape Grim (Australia) in the
Southern Hemisphere. Seasonal variations are similar to the Northern
Hemispheric sites, showing highs in winter and lows in summer. The model
fails to reproduce the observed seasonal trend at Chacaltaya, presumably due
to its high elevation (5421 m a.s.l. on the Andes) that is not well
resolved. At the two Antarctic sites (Fig. 11g, h), the model does not
simulate the seasonal variations well, likely due to a lack of emission
measurement and oversimplified emission fluxes. With all emission scenarios
except SW98, the model underestimates the observations substantially during
warmer seasons (November to February), as also noted by Zhang et al. (2011).
In fact, snow (ice) melting and reforming may enhance 222Rn emissions
and surface concentrations in relatively warmer seasons. SW98 is the only
scenario with prescribed non-zero emission fluxes in the Antarctic. It
arbitrarily assigns a small and fixed value to the emission in the Antarctic
region due to no measurements of soil 226Ra content, but the scenario causes model
overestimates in surface 222Rn concentrations at the two sites,
especially during winter. Evangelista and Pereira (2002) reported summertime
222Rn fluxes ranging between 0.21 × 10-2 and 28 × 10-2 atom cm-2 s-1 during the
summer of 1998/1999 at the Admiralty Bay area of King George Island,
Antarctic Peninsula (62∘ S, 58∘ W). The work also
suggested such low fluxes could not explain 222Rn concentration surges
in the atmosphere. The sparse measurements at the edge of the Antarctic are
not adequate for inferring emission fluxes over the remote continent. More
future measurements of 222Rn emissions in Antarctic regions are thus
desired.
Vertical distribution of 222Rn concentrations
The vertical distribution of 222Rn reflects mainly the convective
transport process rather than large-scale advection due to the relatively
short decay lifetime (a few days) of 222Rn. However, it is more
challenging for global models to capture the convective transport of
222Rn concentrations to the middle and upper troposphere than the
synoptic-scale transport (Jacob et al., 1997). In this section, we
characterize the convective transport in GEOS-Chem driven by the MERRA and
GEOS-FP meteorological datasets, respectively, and evaluate model
simulations with observed 222Rn vertical profiles.
Simulated 222Rn profiles and comparison with observations
The most widely used 222Rn profile measurements were compiled by Liu et
al. (1984) (black line, Fig. 12a). The composed profile is averaged from
222Rn observations over the United States, Ukraine, and central Asia and
represents the summer 222Rn vertical distributions over northern
midlatitude continental regions. The profile shows an inflection point
between 3 and 4 km, reflecting the average altitude of convective
entrainment (Fig. 12a). Concentrations decrease slowly as altitude increases
from 4 to 7 km, indicating fast convective transport over land during summer
(Liu et al., 1984; L. Zhang et al., 2008). We sample the simulated monthly mean
222Rn profiles at the provinces or states where each observed profile
was measured and obtain an average profile for each simulation. As shown in
Fig. 12, all simulations capture the rapid decrease of 222Rn
concentrations from the surface to about 4 km well, at a rate of 1000 mBq SCM-1 km-1. The simulated concentrations then decrease faster than the observations
until 6 km. This is suggested to be a consequence of overly vigorous
convective transport in the model with detrainment at overly high altitudes
(Considine et al., 2005). MERRA exhibits a higher and deeper convection from
5 to 10 km. As a result, a remarkable underestimation of 222Rn
concentrations with MERRA is seen from 4 to 8 km, followed by overestimates
above 9 km. Deep convective cloud top in MERRA has been shown biased high
compared to CERES-observed clouds (Posselt et al., 2012). Stanfield et al. (2019) found that the frequency distribution of convective entrainment rates
(mixing between environmental air with in-cloud air) for deep convection
events in GEOS-5 has a significantly larger fraction in the higher end
values compared to the rates derived from CO profiles observed by the Tropospheric Emission Spectrometer (TES) and Microwave Limb Sounder (MLS).
Intensive mixing within convective updrafts undermines the upward lifting of
surface air masses to the upper troposphere, possibly causing the rapidly
decreasing 222Rn concentrations with height in the simulation with
GEOS-FP. Due to weaker convection in GEOS-FP, the simulation underestimates
in a broader altitude range (4–10 km). It seems challenging for the two GEOS
products to capture the convective detrainment level. As pointed out below,
weaker convection in GEOS-FP at the resolution of 2∘× 2.5∘ is partially due to the transport errors introduced by using
the archived and regridded meteorological data
(Yu et al., 2018).
Comparison of vertical of 222Rn profiles (mBq/SCM) simulated
with four emission scenarios (simulations A1, A2, A3, and A4; see Table 2)
with (a) an average profile compiled from 23 locations over the Northern Hemisphere continents (Liu et al., 1984); (b) an average summertime profile constructed from measurements at Moffett Field (37.4∘ N, 122∘ W), California (Kritz et al., 1998); (c) an average summertime profile from measurements on the east coast of Canada during the 1993 NARE campaign (Zaucker et al., 1996); and (d) an averaged profile measured in May
of 2006–2008, at Goulburn (34.8∘ S, 149.7∘ E), New South
Wales, Australia (Williams et al., 2011). In panel (a), more than half of the observed profiles reach up to 6–12 km. Horizontal bars indicate the standard deviations of the observed 222Rn concentrations.
Figure 12b compares model results with the 222Rn profile averaged from
measurements obtained at Moffett Field, a coastal site in California, United States,
during June to August in 1994 (Kritz et al., 1998). The model profiles are
obtained by averaging monthly 222Rn concentrations in the grid column
corresponding to the site and those in the grid column to the west as
suggested by K. Zhang et al. (2008). The simulations marginally capture the
“C” shape profile, a sign of strong convective transport in summer. The
simulation with JA97 performs better until up to 5 km, above which those
with ZKC and SW98 agree better with the observations. The large
overestimation at 2 to 5 km with ZKC and SW98 is likely due to
shallow convection that is too strong and/or high emission fluxes. The differences in
near-surface concentrations between the simulations with ZKC and SW98 (Fig. 12b) are caused by averaging ZK11 and SW98 emission fluxes along the edges
of the continent in the formulation of ZKC.
Figure 12c shows the comparison of model simulations with the profile
averaged from aircraft measurements in the east coastal region of Canada
during NARE in August 1993 (Zaucker et al., 1996). The model results are
averages over a region of 41–46∘ N and 60–70∘ W. The
simulation with JA97 reasonably reproduces the observations between 0 and 4 km,
while the simulation with ZKC overestimates between 2 and 5 km. The model
performance for NARE is similar to that for Moffett Field. The stronger
emissions (ZKC and SW98) tend to result in overestimates in the lower free
troposphere (Moffett Field and NARE) but better estimates in the upper
troposphere (Moffett Field).
The vertical 222Rn profiles at Goulburn were measured up to about 3.5 km above ground level in May 2006–2008 (Fig. 12d; Williams et al.,
2011). The corresponding model results are monthly means for May of the
simulation year. The model underestimates the concentrations substantially
but simulates the vertical gradient well. It suggests that the
underestimation is more likely caused by potentially low biases in the
emissions over the Australian continent rather than errors associated with
vertical mixing in the model. Despite this, the model reproduces the
seasonality well in surface 222Rn observations at Cape Grim (Fig. 11f),
which is located on the island of Tasmania to the south of the Australian
continent. Above 2.5 km, the vertical gradient of 222Rn concentrations
decreases in both the observations and the model.
Two model uncertainties may affect our simulated 222Rn profiles: the
remapping of the meteorological data from the original cubed-sphere grid in
the parent GCM (GEOS-5) to an equally rectilinear (latitude–longitude) grid
in the offline CTM (GEOS-Chem) and the degradation of the temporal and spatial
resolutions of the meteorological archive (Yu et al., 2018). Yu et al. (2018)
demonstrated that such remapping and using 3-hourly averaged wind archives
may introduce 5 %–20 % low biases into vertical transport of 222Rn,
including the weakened transport from the boundary layer to the upper
troposphere. They also showed that degrading the spatial resolution of the
meteorological archive for input to GEOS-Chem further weakened vertical
transport because organized vertical motions in the finer resolution are
averaged out in the coarser resolution. Such biases may partially contribute
to the discrepancies between the simulated and observed 222Rn profiles,
which appear to be larger in the mid-troposphere and upper troposphere (5.5–10 km)
when the model is driven by GEOS-FP (Fig. 12). GEOS-FP has finer native
horizontal resolution (0.25∘ latitude by 0.3125∘
longitude) than MERRA reanalysis (0.5∘ latitude by
0.667∘ longitude) and exhibits weaker convection, likely due to a
more intensive regridding. An effort is currently ongoing to restore the
lost vertical transport by implementing the modified Relaxed
Arakawa–Schubert convection scheme in GEOS-Chem (He et al., 2019).
Role of convective transport: MERRA vs. GEOS-FP
To examine the role of convective transport in simulated distributions of
222Rn, we compare model simulations driven by MERRA and GEOS-FP in which
the convective transport operator is turned on or off (i.e., A1, B1, A1-nc,
and B1-nc, where “nc” denotes no convection; Table 2). Figure 13 shows the
latitude–pressure cross-section of zonal mean 222Rn concentrations in
these four simulations averaged over the boreal summer (June, July, and
August). The concentrations are contoured on a logarithmic scale. The strong
gradients above the tropopause in all panels are indicative of a fast
decrease of 222Rn concentrations due to weak vertical diffusion. The
interhemispheric asymmetry in 222Rn distributions reflects the larger
landmass and 222Rn emissions in the Northern Hemisphere. The
latitudinal and vertical distributions of 222Rn concentrations
simulated with MERRA and GEOS-FP are very similar. The overall vertical
transport in the simulation with MERRA is slightly stronger than with
GEOS-FP as shown by the higher 222Rn concentrations near the
subtropical tropopause between 15–30∘ N (Fig. 13a–b). In
contrast, when convection is turned off (Fig. 13c–d), the model simulates
higher 222Rn concentrations near the tropical tropopause with GEOS-FP
than with MERRA, indicating that convection is stronger in MERRA than in
GEOS-FP.
Latitude–pressure cross-sections of zonal mean 222Rn
concentrations averaged over June–July–August (mBq/SCM) as simulated by the
GEOS-Chem model driven by (a) MERRA (A-1), (b) GEOS-FP (B-1), (c) MERRA without convection (A1-nc), and (d) GEOS-FP without convection (B1-nc). Bold black
lines denote the zonal mean tropopause height (hPa) in the corresponding
meteorological dataset.
Percentage changes in zonal mean 222Rn concentrations
averaged over June–July–August due to convective transport in the GEOS-Chem
simulations driven by (a) MERRA and (b) GEOS-FP. Values are (222Rn -222Rnnc)/222Rn × 100,
where 222Rn and 222Rnnc are 222Rn concentrations
simulated with (A1 and B1, Table 2) and without (A1-nc and B1-nc) the
convection operator, respectively.
Comparison of annual zonal mean vertical fluxes of 222Rn
(× 10-22 kg m-2 s-1) in the GEOS-Chem simulations
driven by MERRA and GEOS-FP. (a) Convective fluxes with MERRA,
(b) convective fluxes with GEOS-FP, and (c) difference between panels (a) and (b). (d) Large-scale (LS) vertical fluxes with MERRA, (e) large-scale vertical fluxes with GEOS-FP, and (f) the difference between panels (e) and (f). The
white lines indicate the tropopause height (hPa) in MERRA.
Figure 14 shows the percentage changes in 222Rn zonal mean
concentrations averaged over the boreal summer due to convection in MERRA
and GEOS-FP, defined as (222Rn -222Rnnc)/222Rn × 100 %, where 222Rn and
222Rnnc denote simulations with and without the convection operator,
respectively. Where positive values occur, convection facilitates the
transport of 222Rn to the region and increases 222Rn
concentrations. Similarly, negative values indicate convection decreasing
222Rn concentrations. The negative values in the lower troposphere of
the Northern Hemisphere along with the positives in the middle and upper
troposphere are due to the pumping effect of convection, transporting
surface-emitted 222Rn upward. Convection in the simulation with GEOS-FP
transports about 20 %–30 % less 222Rn to higher altitudes in the
tropics and subtropics compared to MERRA (Fig. 14a vs. b). Figure 15 shows
the annual mean convective and large-scale vertical fluxes of 222Rn in
the simulations with MERRA and GEOS-FP as well as their differences.
Convective fluxes are stronger in a broader latitude range (30∘ S–55∘ N) in the simulation with MERRA. The largest difference
appears in the tropical lower troposphere, where convective fluxes of
222Rn in the simulation with MERRA are about a factor of 2 larger
than those in the simulation with GEOS-FP (Fig. 15c). The large-scale
vertical fluxes of 222Rn in the simulation with GEOS-FP are
significantly larger than those with MERRA (Fig. 15f), partly compensating
for the differences in convective fluxes. This compensation leads to the
aforementioned general similarity in the zonal mean 222Rn distributions
in the two simulations (Fig. 13).
To further illustrate the differences in convective transport between the
simulations with MERRA and GEOS-FP, we show in Fig. 16 the simulated
222Rn profiles averaged over the northern midlatitude land areas
(30–60∘ N) for both cases of with and without
convection. The solid black line with the upper x axis presents the
corresponding concentration ratios between the two simulations. Similar to
the earlier analysis of 222Rn vertical fluxes, convection in MERRA is
stronger as indicated by the large change in 222Rn concentrations at
high altitudes (e.g., 8 km) when convection is off (solid red line vs.
dashed red line). The different characteristics of vertical transport in
MERRA and GEOS-FP are better revealed by examining the 222Rn
concentration ratio profiles (black and green lines with the upper x axis).
Convective transport takes effect from the base of cloud layers (i.e., the
lowest model layer with non-zero convective mass fluxes) in the model,
whereas the large-scale vertical advection occurs from the bottom model
layer up. As shown by 222Rn concentration ratios between the two
simulations with convection turned off (green line, Fig. 16), the vertical
transport of 222Rn through large-scale advection and boundary-layer
mixing is more efficient in GEOS-FP than in MERRA (222Rn ratios
< 1 above ∼ 2.5 km and > 1 below). Even
with convection turned on, simulated near-surface 222Rn concentrations
are still lower in GEOS-FP than in MERRA (solid black line, Fig. 16) because
large-scale advection and boundary-layer mixing dominate near the surface
and drain surface 222Rn faster. When 222Rn reaches the base of
convective clouds, it is more efficiently uplifted in MERRA due to stronger
convection, resulting in lower 222Rn concentrations in the lower
troposphere (222Rn ratios < 1 from ∼ 0.75 to 4 km) and higher concentrations in the middle to upper troposphere
(> 4 km). This feature should also affect the simulations of
other surface-emitted species when using MERRA and GEOS-FP as the driving
meteorology in GEOS-Chem.
Annual zonal mean 222Rn profiles (mBq/SCM, red and blue
lines) averaged over land areas between 30–60∘ N latitudes in
simulations driven by MERRA (A1 and A1-nc, Table 2) and GEOS-FP (B1 and
B1-nc, Table 2), respectively. The black solid line (with the upper axis)
shows the ratios of simulated 222Rn concentrations in the standard
simulations with MERRA and GEOS-FP. The green line shows the same ratios
when convection is turned off in the simulations. The two dotted–dashed black
lines have constant ratios of 1.0 and 1.2, respectively.
Summary and conclusions
We have evaluated the global distributions of 222Rn simulated by the
GEOS-Chem chemical transport model with a focus on the sensitivity of
simulated surface concentrations and seasonality to the choice of available
emission scenarios. A preferred emission scenario was recommended based on
evaluations against surface observation of 222Rn concentrations and
210Pb deposition fluxes. We have discussed the major factors
controlling 222Rn emissions as well as potential emission uncertainties
in East Asia, North Africa, and Antarctic. We have also characterized the
vertical transport processes associated with the MERRA and GEOS-FP
meteorological data products by comparing simulated 222Rn vertical
profiles with observations.
We implemented three new global 222Rn emission scenarios in GEOS-Chem,
SW98 (Schery and Wasiolek, 1998), ZK11 (Zhang et al., 2011), and ZKC (an
optimized inventory modified from Zhang et al., 2011). All scenarios
include prescribed regional variations and seasonality, which are lacking in
the JA97 emission scenario (Jacob et al., 1997) currently used in the
standard GEOS-Chem and other global models. JA97 often led to much larger
biases in surface concentrations relative to the other scenarios because of
lack of spatial variations and overly simplified emission reduction under
freezing conditions (e.g., in high-latitude regions). The new emission
options all resulted in remarkable increases in surface 222Rn
concentrations at northern midlatitudes and high latitudes. Such increases were more
pronounced in winter due to the accumulation effect within the shallow
boundary layer. With constraints from observations, we are able to achieve
much better agreement between the model and observations in all four defined
regions (Europe, Asia, North America, and remote regions) using a customized
emission scenario, ZKC. However, the simulation with ZKC still inherited
some unsolved issues, e.g., large biases in Asia and poorly characterized
emission fluxes in Antarctica and at some elevated sites. More measurements
of soil radium content and surface 222Rn concentrations are desired to
produce a better global 222Rn emission scenario. The seasonality in
surface 222Rn concentrations at northern midlatitudes typically shows
a low in spring and a peak in fall, a result of the competition between
changes in emission fluxes and the strength of vertical transport
(ventilation). In subtropical East and South Asia, the seasonality is
strongly affected by the monsoon and shows a summer minimum. Our analyses also
suggested that 222Rn emissions have been quantified more accurately
over Europe due to more frequent and evenly distributed measurements across
the continent.
We specifically investigated the underestimated Asian 222Rn emissions
and explored possible reasons based on previous studies. Both our simulated
surface 222Rn concentrations and 210Pb (decay daughter of
222Rn) deposition fluxes over Asia suggested underestimated Asian
222Rn emissions in the model. In the simulation experiments with Asian
222Rn emissions scaled up by a factor of 1.2 to 1.7, agreement with
surface observations was significantly improved. However, due to limited
knowledge about the spatial distributions and extents associated with the
underestimation in Asian emissions, we did not apply a larger scaling
factor, which would cause large overestimates of 210Pb deposition
fluxes in the model. As a trade-off, we used a scaling factor of 1.2 for
emissions over China in the ZKC inventory, which increased the simulated
surface 222Rn concentrations and led to a better agreement with
observations in Asia. The issue of underestimated Asian emissions is still
open. An ideal solution would be an improved and spatially resolved emission
map instead of using a uniform scaling factor for the region. The excessive
222Rn emissions in Asia may be due to multiple factors, including
various surface textures, high contents of radium in the soil, active crust
movement along the Asian earthquake zone, and high contents of radium in the
fertilizer used in East Asia and India.
We found that it was challenging for model simulations driven by GEOS
products to fully capture the vertical structure of observed 222Rn
profiles. A comparison with summertime continental profiles showed that both
MERRA and GEOS-FP have biased levels of convective detrainment. Convection
in both MERRA and GEOS-FP was likely too deep in northern midlatitude land
areas. The weak convection in GEOS-FP leads to large low biases of
222Rn in the middle–high troposphere. This is partly attributed to the
lost vertical transport as a result of the remapping from the cubed-sphere
to equally rectilinear grids and the degradation of the spatiotemporal
resolution of the input meteorological data (Yu et al., 2018). A comparison
of global 222Rn vertical distributions between the simulations driven
by MERRA and GEOS-FP showed a distinct difference in the role of convective
transport (versus large-scale vertical advection) in determining the
222Rn vertical distributions. The stronger convective transport in
MERRA is partially compensated for by its weaker large-scale upward advection
compared with GEOS-FP, resulting in similar vertical 222Rn
distributions in the model simulations driven by the two meteorological
products. This has important implications for using chemical transport
models to interpret the transport of other trace gases and aerosols when
these GEOS products are used as driving meteorology.
Data availability
The 222Rn emission data used in this paper are described in Sect. 2.2. Observational data for model evaluation are introduced in Sect. 2.3. All model output, emission data, and observational datasets are available online (10.5281/zenodo.3942287; Zhang et al., 2020).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-1861-2021-supplement.
Author contributions
BZ and HL designed the study. BZ conducted the model simulations and led the analysis. BZ and HL wrote the manuscript, with contributions from all coauthors. JHC, GC, and TDF contributed to the analysis of model results in comparison with observational datasets. SC, CHK, and AGW contributed 222Rn surface and profile datasets. KZ contributed the ZK11 222Rn emission data and surface
observational datasets. DBC contributed to the analysis of 222Rn
vertical profiles. MPS and RMY contributed to the model development.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors thank the editor and two anonymous referees for their comments. Hongyu Liu would like to thank Daniel Jacob for his comments on an
earlier version of the manuscript and Andrea Molod for useful discussions.
The Pacific Northwest National Laboratory is operated for DOE by Battelle
Memorial Institute under contract DE-AC06-76RLO1830. NASA Center for
Computational Sciences (NCCS) provided supercomputing resources. The
GEOS-Chem model is managed by the Atmospheric Chemistry Modeling Group at
Harvard University with support from NASA ACMAP and MAP programs. The
GEOS-Chem Support Team at Harvard University and Dalhousie University are
acknowledged for their effort.
Financial support
This research has been supported by the NASA Atmospheric Composition Campaign Data Analysis and Modeling program (ACCDAM) managed by Hal Maring (grant no. NNX14AR07G).
Review statement
This paper was edited by Yves Balkanski and reviewed by two anonymous referees.
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