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Constraining annual and seasonal radon-222 flux
density from the Southern Ocean using radon-222
concentrations in the boundary layer at Cape Grim
W. Zahorowski, A. D. Griffiths, S. D. Chambers, A. G. Williams, R. M. Law, J.
Crawford & S. Werczynski
To cite this article: W. Zahorowski, A. D. Griffiths, S. D. Chambers, A. G. Williams, R. M. Law, J.
Crawford & S. Werczynski (2013) Constraining annual and seasonal radon-222 flux density from
the Southern Ocean using radon-222 concentrations in the boundary layer at Cape Grim, Tellus B:
Chemical and Physical Meteorology, 65:1, 19622, DOI: 10.3402/tellusb.v65i0.19622
To link to this article:  https://doi.org/10.3402/tellusb.v65i0.19622
? 2013 W. Zahorowski et al. Published online: 14 Feb 2013.
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Constraining annual and seasonal radon-222 flux density
from the Southern Ocean using radon-222 concentrations
in the boundary layer at Cape Grim
By W. ZAHOROWSKI
1
,A.D.GRIFFITHS
1
*, S. D. CHAMBERS
1
,A.G.WILLIAMS
1
,
R. M. LAW
2
, J. CRAWFORD
1
and S. WERCZYNSKI
1
,
1
Australian Nuclear Science and
Technology Organisation, Locked Bag 2001 Kirrawee DC NSW 2232, Australia;
2
Centre for Australian Weather
and Climate Research, CSIRO Marine and Atmospheric Research, PMB 1, Aspendale, VIC 3195, Australia
(Manuscript received 26 August 2012; in final form 17 December 2012)
ABSTRACT
Radon concentrations measured between 2001 and 2008 in marine air at Cape Grim, a baseline site in north-
western Tasmania, are used to constrain the radon flux density from the Southern Ocean. A method is
described for selecting hourly radon concentrations that are least perturbed by land emissions and dilution by
the free troposphere. The distribution of subsequent radon flux density estimates is representative of a large
area of the Southern Ocean, an important fetch region for Southern Hemisphere climate and air pollution
studies. The annual mean flux density (0.27 mBq m
C282
s
C281
) compares well with the mean of the limited number
of spot measurements previously conducted in the Southern Ocean (0.24 mBq m
C282
s
C281
), and to some spot
measurements made in other oceanic regions. However, a number of spot measurements in other oceanic
regions, as well as most oceanic radon flux density values assumed for modelling studies and intercomparisons,
are considerably lower than the mean reported here. The reported radon flux varies with seasons and, in
summer, with latitude. It also shows a quadratic dependence on wind speed and significant wave height, as
postulated and measured by others, which seems to support our assumption that the selected least perturbed
radon concentrations were in equilibrium with the oceanic radon source. By comparing the least perturbed
radon observations in 2002C12003 with corresponding ?TransCom? model intercomparison results, the best
agreement is found when assuming a normally distributed radon flux density with sC300.075 mBq m
C282
s
C281
.
Keywords: atmospheric radon, radon flux density, ocean, Southern Ocean, Cape Grim
1. Introduction
Atmospheric radon-222 (radon) is a passive tracer fre-
quently used for testing, validating, and comparing general
circulation models (e.g. Jacob and Prather, 1990; Rind and
Lerner, 1996; Dentener et al., 1999; Rasch et al., 2000; Law
et al., 2008). Modelling radon-222 seems to deliver results
that are consistently in better agreement with observations
than other atmospheric species of interest due to its simple
source and sink (Law et al., 2010). An important aspect of
using radon as a tracer of atmospheric transport is knowl-
edge of its source function, i.e. of terrestrial and oceanic
radon flux densities.
The Southern Ocean is an important fetch area for four
key Southern Hemisphere atmospheric observation sites:
Cape Grim (40.6838S, 144.6898E), Lauder (45.0388S, 169.
6848E), Cape Point (34.3578S, 18.4978E), and Amsterdam
Island (37.7968S, 77.5728E), aswell as for other observation
sites in Antarctica and the sub-Antarctic. The air from
over the Southern Ocean is often used to establish baseline
concentrations for a number of naturally occurring and
anthropogenic atmospheric species.
In this paper, we have attempted to constrain the annual
and seasonal oceanic radon flux from the Southern Ocean
using atmospheric radon observations at Cape Grim.
To date, the majority of modelling studies that have
employed radon as a passive tracer have approximated
the radon flux for all ice-free land surfaces with a single
value (e.g. Jacob et al., 1997; Law et al., 2008), largely due
to a lack of better information. Over the past 10 years,
*Corresponding author.
email: Alan.Griffiths@ansto.gov.au
TellusB 2013. #2013 W.Zahorowski et al.This is anOpen Access articledistributed under the termsof the Creative CommonsAttribution-Noncommercial 3.0
Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction in any medium, provided
the original work is properly cited.
1
Citation: Tellus B 2013, 65, 19622, http://dx.doi.org/10.3402/tellusb.v65i0.19622
PUBLISHED BY THE INTERNATIONAL METEOROLOGICAL INSTITUTE IN STOCKHOLM
SERIES B
CHEMICAL
AND PHYSICAL
METEOROLOGY 
(page number not for citation purpose)
however, considerable advancements have been made in
our knowledge of the geographical and seasonal variability
of terrestrial radon flux densities; including, but not limited
to, detailed representation of latitudinal gradients (Conen
and Robertson, 2002; Williams et al., 2009), and regional
or global radon flux density maps (e.g. Goto et al., 2008;
Zhuo et al., 2008; Szegvary et al., 2009; Griffiths et al.,
2010). Less effort has been spent on characterising the
radon flux density of the world?s oceans; attributable, at
least in part, to oceanic radon flux densities being much
lower than typical terrestrial flux densities.
There have only been a limited number of experimental
estimates of radon flux density from the world?s oceans.
For the Southern/Antarctic Ocean in particular, the area of
interest in this paper, to our best knowledge, experimental
estimates of radon flux density are limited to 12 spot
measurements (Hoang and Servant, 1972). Likewise, other
oceanic regions are poorly represented in the literature.
This raises the question of representativeness of such
results, given that no reliable annual, or even seasonal,
averages can be derived from the available spot measure-
ments. This problem is exacerbated by the fact that radon
spot measurement methods are biased towards low wind
speed/low wave height conditions, and that they are both
costly and labour intensive to make. Notwithstanding
these limitations, spot measurements provide a valuable
indication of the flux levels to be expected for different
oceanic areas. An account of these measurements is given
in Section 5.1.
In the present study, we address the limitations of spot
measurements by deriving the oceanic radon flux density of
a large region of the Southern Ocean, namely, the fetch
area corresponding to hourly atmospheric radon concen-
tration observations at Cape Grim representing the least
perturbed marine air. Furthermore, our chosen approach
has allowed us to constrain the mean annual and seasonal
oceanic fluxes for the study area.
The outline of the paper is as follows: we briefly describe
Cape Grim?s baseline (oceanic) fetch, and general char-
acteristics of baseline radon observations at the site
(Section 2). Next, we outline the steps for selecting the least
perturbed baseline radon events (Section 3), and evaluate
the radon oceanic flux density (Section 4). Discussion and
conclusions (Sections 5 and 6, respectively) end the paper.
All reported findings are based on the 2001C12008 hourly
radon observations at Cape Grim. Quoted seasons are
Southern Hemisphere seasons defined as follows: summer
(December, January and February), autumn (March, April
and May), winter (June, July and August) and spring
(September, October and November).
We frequently use trajectories corresponding to hourly
atmospheric radon measurements at Cape Grim. Ten-
day back-trajectories were generated using the PC version
of HYSPLIT v4.0 (HYbrid Single-Particle Lagrangian
Integrated Trajectory; http://www.arl.noaa.gov/documents/
reports/hysplit_user_guide.pdf) with the 0.58 GDAS opera-
tional analyses. A discussion of uncertainties associated
with HYSPLIT can be found in Draxler (1991), and
Stohl (1998) reviews the accuracy of trajectory techniques
in general.
2. Study domain and radon measurements
Cape Grim Baseline Air Pollution Station (40.6838S, 144.
6898E) is a coastal site located in north-western Tasmania,
Australia. Itis akey SouthernHemisphere site oftheWorld
Meteorological Organisation?s Global Atmosphere Watch
program. Observations at the site include an extensive
range of greenhouse gases, ozone-depleting chemicals, and
aerosols, with some records dating back to the late 1970s.
In 1998, an advanced radon detector, capable of measuring
the very low radon concentrations characteristic of marine
air, was installed at the site. This instrument, and the
upgrades that followed, enabled the collection of a unique
multi-year data set of hourly radon concentrations with
a precision adequate for estimating oceanic radon flux
density.
2.1. Cape Grim atmospheric radon observations
Radon concentrations measured at Cape Grim depend
strongly on wind direction and can be used to clearly define
a baseline sector (e.g. Fig. 1a). The choice of baseline sector
boundaries in Fig. 1 (1908C12808) follows a traditionally
used definition of baseline sector at the site. We will
henceforth use the term ?baseline (radon) observations? to
signify hourly radon concentrations measured at Cape
Grim when the local wind direction was within baseline
sector; by contrast, the term ?baseline event? will refer to
a set of consecutive hourly baseline observations, i.e. a
baseline event starts when the local wind direction changes
to baseline sector and finishes when it leaves the sector.
The amplitude of Cape Grim angular radon distribu-
tions is dominated by Australian mainland and, to a lesser
extent, Tasmanian emissions (Fig. 1a); on a comparable
scale baseline radon concentrations appear negligible. This
is clearly not the case, however, when the baseline sector
is isolated (Fig. 1b). This figure demonstrates not only
that baseline radon is measureable, but that its angular
distribution within the sector has a distinctive structure
(cf. Section 3.1).
A distinctive characteristic of Cape Grim baseline radon
observations is that they are affected by radon emissions
over a large geographic area (fetch region). The baseline
fetch of the 2001C12008 observations, as delineated by
10-day back trajectories, is shown in Fig. 2a; where density
2 W. ZAHOROWSKI ET AL.
is rendered by a colour contrast signifying the time spent by
trajectories in 50C2950 km grid cells. The same trajectories
are shown in Fig. 2b, only here the colour contrast relates
to the mean time required by air parcels moving along
trajectories to arrive at Cape Grim. While all fetch regions
of Fig. 2b, irrespective of colour, can potentially affect the
composition of Cape Grim air parcels, the radon contri-
bution from blue shaded regions, for example, might be
reduced by up to 1/8 of its initial value, due to radon?s
radioactive decay. Notwithstanding the effect of travel
time, it is evident that baseline radon observations are
affected by air overlying a large portion of the Southern
Ocean.
It is evident from Fig. 2 that some baseline air parcels
spend considerable time over land masses before arriv-
ing at Cape Grim. Given that the oceanic radon flux is
30?E
(a) (b)60?E 90?E 120?E
0?
30?W
60?W
1 10 10
2
10
3
90?W 120?W
150?W
180?
60?S
150?E 30?E
0?
30?W
60?W
Mean time from Cape Grim (in units of 
222
Rn half-lives)Time spent by trajectories in grid cell (h/2500 km
2
)
90?W 120?W
150?W
30?S
60?S
180?
150?E
120?E90?E60?E
Fig. 2. 2001C12008 back trajectory density functions for all baseline observations at Cape Grim. The density is defined by (a) the time
spent by trajectories in 50C2950 km grid cells; (b) the mean time required by air parcels moving along trajectories to reach Cape Grim
(in units of radon half-lives, T
1/2
C303.82 days).
Radon
(mBq/SCM)
Baseline
Mainland
330
300
270
240
210
180
150
120
90
60
30
280
260
240
220
200
180
0
4000
2000
Upwind direction (degrees)
0
Upwind direction (degrees)
Radon
(mBq/SCM)
200
100
(a) (b)
Fig. 1. Annual composite angular radon concentration (mBq/SCM) distributions at Cape Grim for 2001C12008, characterised by 25th,
50th, and 75th percentiles: (a) all wind directions; (b) baseline sector only. Bin width is 5 deg.
OCEANIC RADON-222 FLUX 3
significantly smaller than that from land (e.g. Wilkening
and Clements, 1975), a considerable overlap between base-
line and non-baseline distributions of radon concentra-
tions is to be expected. Quantifying this overlap for the
2001C12008 observations (see Table 1) makes it clear that
a considerable portion of the intervening baseline radon
observations are significantly influenced by continental
emissions.
Another fortuitous aspect of baseline observations at
Cape Grim is that they occur frequently in all seasons; the
percentage of total hourly baseline observations in summer
and spring is 27% each, and in winter and autumn is 23%
each (cf. Table 2, Set 0). The duration of baseline events
typically varies from a few hours to 4 days. The frequency
and duration of baseline events are largely controlled by
the passage of synoptic-scale weather systems.
2.2. Radon measurements: technique and limitations
The measurement technique used to determine hourly
atmospheric radon concentrations at Cape Grim is based
on the dual flow loop, two-filter method (Whittlestone and
Zahorowski, 1998). Since baseline radon concentrations
can be 10 mBq m
C283
or lower (Tables 1 and 2), we have
used a large delay volume (5 m
3
) and multiple, rather
than single, sensing head configurations throughout the
2001C12008 period (2, 4 and 8 heads in 2001C12004, 2005C1
February 2007, March 2007C12008, respectively). The un-
certainty associated with hourly radon estimates for
concentrations above 70 mBq m
C283
is less than 912%;
uncertainties are progressively higher for lower concen-
trations where counting errors begin to dominate. For
instance, at 10 mBq m
C283
the counting error is approxi-
mately 40%, and rises to approximately 100% at 4 mBq
m
C283
. Calibrations were performed every three to four
months until July 2003 and monthly thereafter. Instru-
mental background was determined sporadically until July
2003, and every 3 months thereafter. Sensing heads have
been replaced every 3 years to minimise the accumulation
of lead-210 on the second filter. Sampling is conducted
from a height of 70 m above ground level (164 m above sea
level). The overall recovery rate for hourly radon concen-
trations over the 8-year period was approximately 96%.
We express all Cape Grim radon concentrations in mBq/
SCM, where SCM stands for standard cubic meter; the
convention adopted for standard conditions was pC3010
5
Pa
and TC30213.16 K. Unless otherwise stated, when discussing
distributions of radon concentrations we will characterise
them by seven percentiles (5th, 10th, 25th, median, 75th,
90th, and 95th), since the often subtle changes discussed
do not notably affect means. Means are shown only as
required to facilitate comparison with results obtained by
other research groups.
3. Least perturbed baseline radon observations
Relating baseline radon observations to radon oceanic flux
density can only be done in a straightforward manner for
radon concentrations where the corresponding air parcels
are in equilibrium with their oceanic radon source. How-
ever, as we have previously indicated, a significant frac-
tion of baseline radon observations has been affected
(or perturbed) by radon emissions from land. In general,
perturbations can be positive or negative, i.e. can lead to
higher or lower radon concentrations compared with those
in equilibrium with the oceanic radon source. Perturbations
of either sign, and the methods leading to identification
of baseline observations departing from equilibrium with
the oceanic radon source, are discussed in this section.
Removal of these observations is achieved in seven steps
leading to a final set of least perturbed baseline radon
observations.
3.1. Positive perturbations
We begin by identifying baseline observations affected by
radon emissions from ice- and snow-free land. Toward the
baseline sector boundaries of Fig. 1b the top quartile radon
concentrations more than doubles, whereas the median
concentrations rise only slightly. To identify and exclude
the high-radon air parcels from consideration, we examine
baseline events in the time domain in steps 1 and 2 below.
Step 1. Ideally, the distribution of radon concentrations
should be examined for consecutive hours (or another suit-
able period) after change to baseline. However, by doing
this considerable scatter occurs in the higher percentiles of
Table 1. Radon concentration (mBq/SCM) distribution at Cape Grim in 2001C12008 for baseline (local wind direction between 1908 and
2808) and non-baseline conditions
Radon concentration in air (mBq/SCM)
Wind sector Mean No. of observations 5th 10th 25th Median 75th 90th 95th
Baseline 176 28622 8 15 26 42 96 390 850
Non-baseline 976 38799 28 45 121 378 1164 2881 4101
4 W. ZAHOROWSKI ET AL.
Table 2. Radon concentration (mBq/SCM) distributions of baseline observations in composite year and season at Cape Grim in
2001C12008
Radon concentration in air (mBq/SCM)
Set Season Mean No. of observations 5th 10th 25th median 75th 90th 95th
Summer 151 7792 4 9 20 34 75 375 808
Autumn 241 6523 12 18 31 50 149 605 1273
0 Winter 170 6484 16 21 32 46 94 350 731
Spring 151 7823 7 13 24 40 84 292 651
All 176 28622 8 15 26 42 96 390 850
R1 All 229 19927 8 16 28 48 152 598 1121
Summer 31 2401 4 8 16 26 37 54 71
Autumn 62 2048 11 15 25 39 57 121 203
1 Winter 74 2075 14 21 31 43 61 167 291
Spring 48 2171 7 11 20 31 53 92 133
All 53 8695 8 13 22 34 52 91 173
R2 All 60 3416 7 12 21 34 55 114 221
Summer 29 1473 5 8 16 24 35 52 67
Autumn 54 1374 12 16 26 39 56 96 171
2 Winter 70 1224 17 23 33 43 59 124 245
Spring 43 1208 8 12 20 31 51 77 111
All 48 5279 9 13 22 34 51 79 135
R3 All 194 208 35 48 87 165 241 389 507
Summer 29 1458 4 8 16 24 35 51 64
Autumn 48 1299 11 16 25 38 53 71 121
3 Winter 53 1109 17 22 32 41 53 79 118
Spring 43 1205 8 12 20 31 50 76 111
All 42 5071 8 13 21 33 48 68 99
R4 All 61 426 4 10 17 33 70 155 201
Summer 28 1341 5 9 16 25 35 49 61
Autumn 45 1171 11 15 25 38 51 66 84
4 Winter 52 1047 18 23 32 41 53 73 106
Spring 40 1086 8 12 20 30 50 72 96
All 41 4645 9 13 22 33 47 65 85
R5 All 39 988 9 13 23 34 47 63 79
Summer 29 1170 5 9 16 25 35 49 61
Autumn 45 903 10 14 24 36 52 67 85
5 Winter 55 751 20 25 33 43 54 77 114
Spring 41 833 8 12 20 30 50 74 102
All 41 3657 9 13 21 33 47 66 88
R6 All 38 1267 9 13 20 32 46 65 86
Summer 28 920 5 9 17 25 35 49 61
Autumn 46 736 11 15 25 36 51 68 93
6 Winter 61 442 24 27 35 44 55 84 114
Spring 49 292 7 14 23 37 58 77 117
All 42 2390 8 13 22 33 48 66 88
R7 All 39 1490 7 12 21 32 47 65 77
Summer 31 361 9 12 18 27 36 51 62
Autumn 56 219 14 19 29 40 55 71 212
7 Winter 49 207 25 28 36 44 52 81 104
Spring 53 113 7 13 22 32 53 100 163
All 44 900 11 15 23 35 48 66 99
A sequence of concentration sets (from top to bottom) characterises the results of selection steps for obtaining the least perturbed radon
concentration set. The sequence starts with the initial baseline observation set defined by the local wind direction only. This is followed by
seven sets each of which is obtained by removing some observations from the previous set. Annual distributions of the removed
observations (R1C1R7) are also shown. See Section 3 for details; bold entries are depicted in Fig. 5.
OCEANIC RADON-222 FLUX 5
events persisting for longer than 40C150 hours, as they are
represented by progressively smaller numbers of observa-
tions. To avoid this scatter we examine distributions of
baseline observations as a function of the minimum length
of time they persist in the sector (Fig. 3).
A significant, and consistent, decrease in radon con-
centrations is evident for baseline observations within the
first 12 hours after change to baseline. A less pronounced
decrease then continues until 24 hours into baseline con-
ditions, after which time the distribution range reaches a
minimum that can be considered a first approximation of
least perturbed oceanic air. Based on the above findings, we
remove all observations persisting in the baseline sector for
less than 24 hours.
Step 2. For events persisting longer than 36 hours in
the sector there is a small widening of radon distributions,
mainly due to higher concentrations in the top 10 per cent
of observations. This is likely attributable to the fact
that at the end of a baseline event, there is an increased
likelihood of land radon emissions becoming entrained
in the corresponding air parcels that are still nominally
baseline. The associated change in air parcel character-
istics, such as the observed increase in land perturbation,
is gradual. To quantify such perturbations at the end of
baseline events we examine radon concentration distribu-
tions binned as a function of time until the end of the
baseline event (Fig. 4). This figure shows a considerable
widening of baseline radon distributions in the last 12
hours that an event spends in the baseline sector. Accord-
ingly, we remove all observations made within the last
12 hours in the baseline sector.
The results of steps 1 and 2, as well as the selections
steps that follow, are documented in Fig. 5: each step is
represented by a graph showing the radon distributions
before and after the selection (left and right distribu-
tions, respectively) as well as the distribution of rejected
observations (the middle distribution). The graphs also
show the number of the rejected events expressed as a
percentage of the number of events in the initial baseline
set (set 0 in Table 2). Numerical results for each step are
also shown in Table 2. Note that the radon distributions
of observations rejected in steps 1 and 2 (Fig. 5a and 5b,
respectively) are clearly higher than the distributions before
and after the respective steps.
Examination of the events remaining after steps 1 and
2 revealed there were still some high radon observations
clearly affected by land emissions. In steps 3 and 4 we
identify and exclude these remaining observations using
back trajectory analysis.
Step 3. First we removed all observations for which cor-
responding trajectories indicated any contact with snow-
and/or ice-free land (i.e. excluding Antarctica) within 10
days before reaching Cape Grim. Only a limited number
of events, 0.7% of the initial set, were directly affected by
land. Consequently, although their radon distribution is
relatively high (Fig. 5c, middle distribution), the reduction
in distribution range is small compared with those in
steps 1 and 2 (cf. Fig. 5a and 5b). It is of note, however,
that the distribution of observations removed in step 3 is
more than twice as wide as that of step 2. This is not
surprising as the latter quantifies the changes air parcels
undergo while exiting baseline sector, whereas the former
deals with changes affected by direct contact with land.
The small number of observations rejected in this step is
due to the fact that most of the land-affected events were
accounted for in steps 1 and 2.
Step 4. We also investigated whether air parcels on their
way to Cape Grim may have had their radon concentra-
tion affected by mixing with off-shore radon-rich air.
The strength and geographical extent of land influence on
offshore radon concentrations is best quantified directly
by ship radon transects. We addressed the current lack of
suitable ship transects in our study domain by investigating
how close air parcels travelled to the southern shore of the
Fig. 3. Radon concentration (mBq/SCM) distribution charac-
terised by 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles in
the baseline sector as a function of duration in the baseline sector.
Fig. 4. Radon concentration (mBq/SCM) distribution charac-
terised by 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles in
the baseline sector in consecutive 3-hour period before a change to
a non-baseline sector.
6 W. ZAHOROWSKI ET AL.
Australian mainland. We assumed that this region is
most likely to contribute to the effect based on the pre-
vailing airflow in the baseline sector (cf. Fig. 2a and 2b).
We estimated the magnitude of the effect by identifying
baseline trajectories traversing within 28 latitude of the
Australian coast and analysing the radon distributions
of observations that mixed progressively less with the
radon-rich offshore air, as determined by the number of
hours in the last 10 days the corresponding trajectories
spent in that band. We found a discernible effect of this
mixing in that the change is most pronounced in autumn
and negligible in summer. In autumn, the mixing affects the
top 5% of observations by reducing the 95th percentile
from 121 mBq/SCM to 84 mBq/SCM (Sets 3 and 4 in
Table 2). Similar to the previous step, the number of the re-
jected observations is small (1.5% of the initial baseline set)
1200
800
(a)
69.6% 11.9%
1.5%
4.4%
0.7%
3.5%
5.2%
(b)
(c)
(d)
(e)
(f)
(g)
400
300
200
100
300
200
100
100
50
0
5R66
0
3R44
0
1R22
600
400
200
100
50
100
50
0
6 R7 7
0
4 R5 5
0
2R33
0
0 R1 1
Radon concentr
ation (mBq/SCM)
Fig. 5. Radon concentration (mBq/SCM) distributions for the seven selection steps to extract from the initial baseline observations
(Set 0 in Table 2) a set of least perturbed radon concentrations in air parcels that are in equilibrium with their oceanic radon source (Set 7 in
Table 2). Each step is represented by a graph contrasting a distribution of rejected concentrations (in red) with associated distributions
before and after application of the step?s selection criterion shown to its left (in black) and right (in green). The number of rejected
concentrations expressed as a percentage of the number of events in the initial baseline set (Set 0 in Table 2) is shown in the top right corner.
All distributions are identified by their numbers shown in Table 2. Note change of the radon concentration scale between different graphs.
OCEANIC RADON-222 FLUX 7
and the distribution of the rejected concentrations signifi-
cantly wider (the middle distribution in Fig. 5d and Set R4
in Table 2) and comparable to that of Step 2.
The above four steps combined led to a 10, 6, and 2 times
reduction of the 95th, 90th, and 75th percentiles of the
annual distribution, respectively. Note, however, that the
overall reduction in the median is only 20% (Table 2).
3.2. Negative perturbations
We now examine effects that can potentially result in
negative perturbations of observed baseline radon con-
centrations. The first two relate to an air parcel?s radon
composition being affected by contact with land covered
with snow and/or ice sheets (Step 5), or by contact with
ice-covered ocean (Step 6). Such covers (full or partial), will
lead to suppression of radon emissions and possibly lead to
radon flux densities discernibly lower than those that are
characteristic for ice-free ocean. Any extended period spent
over such surfaces might lead to lower radon concentra-
tions observed at Cape Grim.
Step 5. We identified snow/ice affected baseline observa-
tions by selecting events for which corresponding air par-
cels traversed Antarctica. The distribution of the excluded
observations is discernibly lower than of the input set (R5
in Fig. 5e), even though air parcels affected by passage
over Antarctica then spent considerable time above the
ocean before arriving at Cape Grim; during that passage,
concentrations can be reduced by 4C18 times (cf. Fig. 2b).
Note that the observed effect might also be due to the
passage of air parcels over ice-covered sea.
Step 6. We found more than 1200 observations
(cf. Table 2) for which trajectories had not traversed
Antarctica, but were over ice-covered sea (Stroeve and
Meier, 2012). Removing these observations had a very
small effect on the spring radon distributions, which was
the season most represented in the removed set (cf. Sets 5
and 6 in Table 2).
Step 7. In the final step, we attempted to account for
dilution of marine boundary layer radon by free tropo-
spheric air, due to large scale air movements across the
top of the boundary layer. Such dilution is possible due
to the relatively short half-life of radon, resulting in a
radon concentration gradient across the top of the marine
boundary layer (e.g. Kritz et al., 1998; Williams et al.,
2011).
To account for such events we excluded observations
corresponding to air parcels that spent more than 30% of
the 10 days before arrival at Cape Grim in the free
troposphere. The overall effect of this exclusion leads
to a non-negligible upward shift of the distribution
(Fig. 5g and Table 2), irrespective of season (not shown).
Imposing any further restriction on the time trajectories
spent within the Marine Boundary Layer (MBL) drastically
reduces the number of observations with a negligible
impact on the resulting radon distribution.
In order to exclude trajectories that had limited contact
with the ocean surface before arriving at Cape Grim, we
defined the weighted fractional time inside the MBL. For a
trajectory s, starting at time t
0
and terminating at Cape
Grim at time T, the weighted fractional time inside the
MBL, T
MBL
, is:
T
MBL
?
1
s
Z
T
t
0
Is;t??e
C0k TC0t??
dt ;
s ?
Z
T
t
0
e
C0k TC0t??
dt ?
1
k
1C0e
C0k TC0t
0
??
C0C1
(1)
where I(s,t)C301ifs is inside the boundary layer at time t,
and 0 otherwise. lC302.0982C2910
C286
s
C281
is the decay con-
stant of radonC1222, and t is the decay timescale associated
with the entire (finite) trajectory. Note that for non-
decaying species (lC300), tC30TC28t
0
.
We postulate that the final set (Set 7 in Table 2, and
Fig. 6, showing the final set?s trajectories), obtained after
application of the seven selection steps, constitutes the least
perturbed baseline radon observations at Cape Grim for
the 2001C12008 period. Accordingly, we use this set below
for estimating the oceanic radon flux. Note that the process
of eliminating perturbed observations also results in a
30?E
0?
30?w
60?S
60?w
1
Time spent by trajectories in grid cell (h/2500 km
2
)
10
10
2
10
3
90?w 120?w
60?E 90?E 120?E
150?w
180?
150?E
Fig. 6. 2001C12008 back trajectory density functions for least
perturbed baseline observations (Set 7 in Table 2) at Cape Grim.
The density is defined by the time spent by trajectories in 50C2950
km grid cells.
8 W. ZAHOROWSKI ET AL.
drastic reduction of available observations after each step
as shown in Fig. 5 and Table 2.
4. Estimation of radon flux density
Having identified a least perturbed set of radon concentra-
tions, we now derive an expression for the mean oceanic
radon flux as a function of the radon concentrations
(Section 4.1), and define other variables required for the
final flux calculation (Sections 4.2 and 4.3).
4.1. Estimation of the flux
The vertically integrated boundary layer radon budget
following an air parcel trajectory can be written as:
dR
dt
? F C0 kR ; R ?
Z
h
0
c?z?dz C17 hchi
z
(2)
where R is the total radon per unit area in the boundary
layer, h is boundary layer depth, c(z) is radon concentra-
tion at height z, and F is the net radon flux density into the
column. For a trajectory starting at time t
0
and terminat-
ing at the point of interest at time T, Equation 2 has the
solution:
R ? R
0
e
C0k TC0t
0
??
?
Z
T
t
0
Ft??e
C0k TC0t??
dt
? R
0
e
C0k TC0t
0
??
? s
C22
F
w
(3)
where R
0
C30R(t
0
) and
C22
F
w
is an exponentially weighted
mean flux:
C22
F
w
?
1
s
Z
T
t
0
Ft??e
C0k TC0t??
dt (4)
For an infinite trajectory, (TC28t
0
)0C12 and s ! 1=k.
Expressing
C22
F
w
as a balance between the mean oceanic
radon flux F
ocean
and a venting flux out of the boundary
layer top F
vent
, we then obtain from Equation 3:
R C17 hchi
z
?
F
ocean
C0F
vent
k
(5)
Assuming radon is uniformly distributed with height
throughout the boundary layer, we set chi
z
? c
sfce
, the
atmospheric radon concentration measured near the sur-
face. Further assuming significantly lower radon above the
boundary layer (Kritz et al., 1998; Williams et al., 2011), we
can parameterise the venting flux as:
F
vent
C25C0w
e
Dc ? w
e
c
sfce
(6)
where w
e
is the entrainment velocity and Dc is the con-
centration jump across the boundary layer top. Substitu-
tion into Equation 5 produces an expression for the mean
oceanic radon flux:
F
ocean
? c
sfce
w
e
?hk?? (7)
We use Equation 7 for oceanic radon flux estimates,
substituting for c
sfce
hourly baseline radon observations
from the least perturbed oceanic set defined in the previous
section. As a result, we obtain a range of flux estimates
reflecting a range of conditions in the 2001C12008 period.
Estimates of the other two variables in Equation 7, the
entrainment velocity w
e
and mixing height h are discussed
below.
4.2. Mixing height at Cape Grim
We used data from the ERA-Interim reanalysis (Simmons
et al., 2007), to estimate mixing depths at Cape Grim
during each of the least perturbed baseline events in 2001C1
2008. The reanalysis model is based on the European
Centre for Medium-Range Weather Forecasts (ECMWF)
operational model, and provides mixing depth output on
a 1.58 grid every 3 hours based on a Richardson Number
definition of PBL (Palm et al., 2005). Data were taken from
the nearest oceanic grid point to Cape Grim, located at
40.58S, 1448E.
The mixing height from various implementations of this
model has been compared with radio-sonde and satellite
observations, generally with encouraging results:
C147 A bias in mixing height of approximately 20 m
(model too low by 2 hPa), with RMS error of 200 m,
was reported by von Engeln et al. (2003) from
86 radio-sonde ascents launched from 10 island
stations, including Amsterdam Island at a similar
latitude to Cape Grim but 5,600 km upwind.
C147 The earlier ERA-15 reanalysis captured the thermal
structure of the boundary layer near 318N, but
cloud cover was strongly underestimated; observed
low cloud cover was 83% compared with the
modelled 22% (Duynkerke and Teixeira, 2001).
C147 Model mixing heights compared well to multi-year
mean mixing height from radio occultation-derived
mixing heights (von Engeln et al., 2005). Compared
with the GLAS satellite lidar, however, the mixing
height in the ECMWF model was an average
of 200C1400 m lower, although it exhibited similar
spatial patterns along satellite tracks (Palm et al.,
2005).
Given the above, there was a reason to test the validity of
the modelled mixing heights near Cape Grim. We achieved
this by comparing model output to mixing heights derived
from the Mini-Lidar data gathered at Cape Grim during
OCEANIC RADON-222 FLUX 9
the second half of 1998 (Young, 2006, 2007). Sixteen
baseline events were selected from June to December
1998 for the comparison. Low clouds prevailed during
these events, consistent with the cloud climatology of this
region (Warren et al., 1988).
To derive mixing heights from the lidar data, the mixing
height was taken to be the top of the lowest cloud layer.
This height was determined manually, as the low signal-
to-noise ratio of the lidar makes alternatives impractical.
As a consequence of the method, the determination of
mixing height was partly subjective, and becomes more so
during periods of ambiguous lidar signals. The analysis
method also suffers from the inability to detect a decoupled
boundary layer beneath low cloud. Nevertheless, lidar and
model mixing heights agree well (Fig. 7). Based on 183 lidar
measurements of mixing height, the lidar-derived mean
mixing height of 956 m is approximately 11% lower than
the ERA-Interim mixing height of 1069 m, with an RMS
error of 217 m.
4.3. Entrainment velocity
For mid-latitudes, the marine boundary layer is typically
shallower than its terrestrial counterpart and exhibits
limited diurnal variability (e.g. Kritz, 1983). The primary
capping inversion that separates MBL processes from the
free troposphere is usually found 1000C11900 m above sea
level (Ayers and Galbally, 1995; Boers et al., 1998; Russell
et al., 1998; Lenschow et al., 1999). When present, cloud
base usually lies between 400 and 800 m, and the overlying
cloud layer is often less than 500 m thick (Russell et al.,
1998; Lenschow et al., 1999). Depending on local mete-
orological conditions, a secondary inversion may form
at C1 or near C1 cloud base, resulting in the formation of a
detached buffer layer between MBL processes and the free
troposphere (Russell et al., 1998).
To a first approximation, the mid-latitude MBL is often
assumed to be capped by an impermeable temperature
inversion (e.g. Raes, 1995). There is, however, an exchange
mechanism (entrainment) that links MBL processes and
products to the free troposphere, and vice versa, which
plays an important role in cloud formation as well as the
budgets of aerosols and trace gas species (Moeng et al.,
1996; Lenschow et al., 1999; Osborne et al., 2000; Sollazzo
et al., 2000). MBL entrainment/detrainment is contributed
to by two processes: (1) cloud venting processes (e.g.
Cotton et al., 1995), and (2) the balance between large
scale subsidence and changes in mixing depth (Kritz, 1983).
Mid latitude MBL entrainment rates, with the exception
of isolated cases (e.g. C2830 to 50 mm s
C281
, Sollazzo et al.,
2000; 6C112 mm s
C281
, Wang et al., 2008), are generally found
to be between 0 and 10 mm s
C281
(Boers and Betts, 1988;
Russell et al., 1998; Lenschow et al., 1999; Wang et al.,
1999b; Yi et al., 2008). Estimates within this range include
6.5 mm s
C281
(Bretherton et al., 1995; Sollazzo et al., 2000),
3C16mms
C281
(Raes, 1995; Clarke et al., 1996) and 0C15mm
s
C281
(Kawa and Pearson, 1989; De Roode and Duynkerke,
1997; Wang et al., 1999a; Hill et al., 2008; Faloona, 2009;
Lock, 2009). The most common estimate for mid-latitude
MBL entrainment for clear and cloudy conditions, how-
ever, is 3C14mms
C281
(e.g. Kritz, 1983; Ayers and Galbally,
1995; Boers et al., 1998; Stevens et al., 2003; Sandu et al.,
2009). In most cases, the uncertainty associated with these
estimates is of the order of 25C160%.
Taking into account the abovementioned published data
and assuming that fairly uniform meteorological conditions
prevailed during the baseline events of the least perturbed
set, we have adopted a constant entrainment rate of
4mms
C281
and assumed a conservative uncertainty of 50%.
5. Results and discussion
Annual and seasonal distributions of the oceanic radon
flux density are summarised in the top section of Table 3.
We start the discussion by comparing our flux results
with those obtained by other researchers either from spot
measurements, or model estimates; commonly assumed
oceanic flux for comparisons of regional or global models
are also included.
500 1000 1500 2000
Lidar mixing height (m)
500
1000
1500
2000
Model mixing height (m)
Fig. 7. ERA-Interim mixing height (labelled model) at the
closest oceanic grid point to Cape Grim compared with mini-lidar
mixing height measurements at Cape Grim during 14 baseline
events in 1998. An orthogonal distance regression fitting line (slope
1.03) shown on the scatter plot explains 85% of the variance.
10 W. ZAHOROWSKI ET AL.
Table 3. Comparison of oceanic radon flux densities (mBq m
C282
s
C281
) obtained in this paper (top section of the table) followed by spot measurements (accumulator and profile techniques),
modelled flux, and assumed in or inferred from general circulation models
Radon-222 flux density (mBq m
C282
s
C281
) Wind speed (m s
C281
)
Source Method Region
10th/50th/90th
percentiles
Mean and
standard deviation No. of samples
Mean and standard deviation
Range 10th/50th/90th percentiles
Summer 0.19090.122 361 12.2/15.2/19.9
a
0.071/0.164/0.311
Autumn 0.34790.37 219 11.0/15.0/21.0
a
0.109/0.273/0.513
This study
Radon
concentrations
in boundary
layer air at
costal site
Southern Ocean
Winter 0.30390.17 207 12.6/16.5/22.0
a
0.167/0.267/0.508
Spring 0.31890.39 113 11.1/15.3/18.8
a
0.081/0.202/0.631
All seasons 0.27090.261 900 12.1/15.7/20.7
a
0.092/0.218/0.429
Flux spot measurements
Wilkening and Clements, 1975 Accumulator Tropical Pacific 0.1690.02 Not reported 3
Duenas et al., 1983 Accumulator West Mediterranean 0.05/0.11/0.30 0.1490.09 36 0.5/2.6/5.6
Broecker and Peng, 1971 Radon profiles North Atlantic trade
wind belt
0.021/0.024/0.029 0.02490.0031 45 5C17.5
Hoang and Servant, 1972 Radon profiles Southern Ocean 0.2490.15 12 6.694.3
Peng et al., 1974 Radon profiles North Pacific 0.0890.03 7 8.993.0
Peng et al., 1979 Radon profiles South Atlantic
b
0.0490.02 9 7.893.3
Smethie et al., 1985 Radon profiles Tropical Atlantic 0.05/0.15/0.30 0.1790.12 29 4.9/7.1/10.0
Kawabata et al., 2003 Radon profiles West North Pacific 0.1290.08 13 15.797.5
Flux model
Schery and Huang, 2004 Flux model Southern Ocean
c
0.04/0.06/0.08 0.0690.02 n/a n/a
Flux assumed for or inferred from models
Jacob et al., 1997 Model All oceans, 608NC1608S 0.11 n/a
Mahowald et al., 1997 Model Southern Ocean 0.06 n/a
Schery and Wasiolek, 1998 Model All oceans and ice
covered regions
0.14 n/a
Taguchi et al., 2002 Model All oceans, 608NC1608S 0.21 n/a
Law et al., 2008 Model All oceans, 708NC1708S 0.11 n/a
a
Distribution of trajectory averaged wind speeds weighted by radon decay.
b
Nine samples from the Southern Atlantic C1 the study reports 100 measurements.
c
Global model, results shown in the table refer to the Southern Ocean only.
OCEANIC
RADON-222
FLUX
11
5.1. Comparison of oceanic fluxes with other
estimates
Over the past four decades, a number of attempts have
been made to experimentally constrain the magnitude of
the radon flux density for different oceanic regions. These
attempts are summarised in Table 3. The experimental
techniques used for the estimates were: (1) direct measure-
ment using accumulation chambers (cf. Wilkening and
Clements, 1975; Duen? as et al., 1983); and (2) using vertical
profiles of oceanic
226
Ra/
222
Rn to infer radon flux to the
atmosphere (Hoang and Servant, 1972; Peng et al., 1974;
Smethie et al., 1985; Kawabata et al., 2003). The means
range from 0.024 mBq m
C282
s
C281
(Broecker and Peng, 1974)
to 0.24 mBq m
C282
s
C281
(Hoang and Servant, 1972).
Of the eight experimental estimates of the oceanic radon
flux density shown in Table 3, only one study (Hoang and
Servant, 1972) specifically targeted the Southern Ocean.
Only 12 spot measurements were made with a mean equal
to 0.2490.15 mBq m
C282
s
C281
. The radium profiles on which
the results of Hoang and Servant (1972) were based
were collected in two cruises that spanned the latitudes
38810?Sto66814?S of the Southern Ocean (Ku et al., 1970).
The northernmost profiles were conducted in AugustC1
September and the southernmost in JanuaryC1February.
The mean radon flux density derived from this study
is equal to 0.27090.261 mBq m
C282
s
C281
, which agrees well
with the mean proposed by Hoang and Servant. The
agreement should be viewed with some caution. Firstly,
we compare the means of a small number of spot flux
density measurements with an average of a large number of
estimates representing large oceanic areas (cf. Table 3).
Secondly, the flux densities we calculate have their own,
significant uncertainty. The most important factor con-
tributing to that uncertainty is the uncertainty associated
with entrainment velocity. We have adopted a constant
entrainment rate of 4 mm s
C281
and have assumed a
conservative uncertainty of 50% (Section 4.3). Hence, at
least the same uncertainty has to be attributed to radon
flux density distributions (cf. Equation 7). Constraining
such an uncertainty could be one of the most effective
future refinements of our results once better constrained
and possibly time dependent entrainment velocity estimates
become available. By contrast, uncertainty due to mixing
height at Cape Grim, interpolated from the ERA-Interim
reanalysis for each hourly event in the least perturbed set is
significantly smaller (a bias of 11%, Section 4.2) and can be
neglected in view of the entrainment velocity uncertainty.
Another possible refinement of our radon flux densities
could result from a reanalysis of the underlying baseline
observations using a multi-particle (e.g. Arnold et al., 2010)
rather than single-particle method for generation of back-
trajectories. We think that such a refinement is possible,
and would have the advantage that dispersion and turbu-
lent mixing processes would be included, but whether it
would result in a significant change of our results is less
certain. Firstly, despite the error from modelling air mass
back-trajectories as single particles (Stohl et al., 2002),
we attribute fluxes to a very large source region, employ
conservative data selection rules, and base results on a large
number of trajectories, all of which allows our method to
tolerate a large uncertainty in the position of individual
back-trajectories. Secondly, any benefit of eliminating this
error would be reduced by the relative scarcity of mete-
orological data in our study domain. And thirdly, uncer-
tainties of 10-day back-trajectories are relatively large
regardless of the method employed for their generation C1
see for instance Draxler (1991); Engstro? m and Magnusson
(2009) and Wen et al. (2012) for experimental and
numerical treatment of the uncertainties associated with
back-trajectories.
Considerable seasonal variability was observed in the
median oceanic radon flux density estimate, from 0.164
mBq m
C282
s
C281
in summer to 0.267 mBq m
C282
s
C281
in winter.
The mean flux density estimate based on our summer
observations (0.1990.12 mBq m
C282
s
C281
) compares well
with flux densities reported from the tropical Pacific
(0.1690.02 mBq m
C282
s
C281
; Wilkening and Clements,
1975)andthetropicalAtlantic(0.17mBqm
C282
s
C281
;Smethie
et al., 1985).
However, a number of other experimentally estimated
flux densities (Table 3) were considerably lower than values
reported in this study. The nature of accumulation
measurements of the oceanic radon flux biases results
toward low wind speed/wave height conditions. Similarly,
the challenges involved in making ship-based profile
measurements in conditions of high seas result in a low-
to-medium wind speed bias on such results. These biases
may explain why many of the existing experimental
estimates of the oceanic radon flux density are lower than
those reported here. Hence, it is possibly not a coincidence
that the two studies that reported making radium profile
observations under conditions of higher wind speeds
(Peng et al., 1974; Kawabata et al., 2003), reported the
upper boundaries of their radon flux density results at 0.12
and 0.23 mBq m
C282
s
C281
, respectively (Table 3), which are
closer to the findings of this study.
Southern Ocean specific radon flux densities that have
been inferred from modelling studies: 0.06 mBq m
C282
s
C281
(Mahowald et al., 1997), 0.06 mBq m
C282
s
C281
(Schery and
Huang, 2004) are more than four times lower than our
mean radon flux density. Of the oceanic flux density
assumed for modelling studies, the ones closer to this study
are those of Taguchi et al. (2002) (0.21 mBq m
C282
s
C281
) and
Schery and Wasiolek (1998) (0.14 mBq m
C282
s
C281
).
12 W. ZAHOROWSKI ET AL.
5.2. Dependence on wind speed, significant wave
height, and latitude
Having established a significant number of hourly estimates
of oceanic radon flux, we are now equipped to look for
a relationship between these radon emissions and other
quantities that we can estimate along a trajectory. In
practice, any relationship will be overwhelmed by noise
unless the quantity under investigation varies smoothly
compared with the uncertainty in trajectory position. We
start with wind speed, which is known to have a strong
effect on oceanC1atmosphere gas exchange (Wanninkhof,
1992; Nightingale et al., 2000; McGillis et al., 2004; McNeil
and D?Asaro, 2007; Jeffery et al., 2008).
In an attempt to consider only the most accurate
trajectories, we exclude points within 4 hours of the start
or end of a least-perturbed baseline event, based on the
premise that trajectories will be more accurate under steady
conditions (Stohl, 1998). We compute wind speed from the
hourly distance travelled by trajectories and then compute
an average along the trajectory after weighting each point
by the radon decay fraction. A plot of calculated flux vs.
wind speed is shown in Fig. 8a.
As it is a more smoothly varying field, and because sea
state has also been shown to affect gas exchange (Woolf,
2005), we perform the same analysis using significant
wave height, extracted from the ERA-Interim Reanalysis
(Simmons et al., 2007). The reanalysis data shows that the
significant wave height within the Southern Ocean fetch
region of this study approximately doubles from summer to
winter. When distributions of radon flux density esti-
mates were calculated for 0.2 m wave height bins, a clear
relationship emerged (Fig. 8b). This result is consistent
with numerous investigations into the effects of waves and
bubble-action on rates of airC1sea trace gas exchange
(D?Asaro and McNeil, 2007; Fangohr and Woolf, 2007;
Rhee et al., 2007; Woolf et al., 2007; Tsoukala and
Moutzouris, 2008).
In both cases a quadratic fit is consistent with observa-
tions, notwithstanding a small number of samples in the
lowest and highest bins. This can be seen as an independent
argument in support of our assumption that the flux
estimates we made based on the least perturbed set are
indeed representative of an actual oceanic flux, and that the
least perturbed radon concentration set (Table 2, Set 7) air
parcels are in equilibrium with the oceanic radon source.
To investigate the possibility of latitudinal dependence,
we grouped the radon flux density estimates according
to the average latitude of the corresponding trajectories
weighted by radon decay (Fig. 9). In summer months
(representing 40% of all observations), radon flux densities
in 18 latitude bins increased southward of 408S to a maxi-
mum value between 52 and 548S (Fig. 9a). This result is
consistent with a similar increase, in summer, in Southern
Hemisphere zonal mean wind speed (based on the ERA-
Interim data) from 30 to 528S (not shown). In non-summer
months (60% of observations), however, radon flux density
estimates displayed no significant dependency on latitude
(Fig. 9b). This is despite the fact that the corresponding
relationship between trajectory speed and latitude shows an
increase in speed towards the pole (0.25 ms
C281
/degree).
5.3. Distribution of oceanic radon flux density
We note that the flux range derived from the selected radon
hourly observations, as has been done in this study, arises
from a combination (or convolution) of variability in the
oceanic emissions, and the effects of transport and mixing.
10 12 14 16
Wind speed (m s
?1
)
18 20 22
2.5 3.0 3.5 4.0 4.5 5.0 5.5
Significant wave height (m)
0.0
0.2
0.4
0.6
0.8
(b)
0.8
(a)
0.6
0.4
0.2
0.0
Radon flux density (mBq m
?2 
s
?1
)
Radon flux density (mBq m
?2 
s
?1
)
Fig. 8. Oceanic radon flux density (mBq m
C282
s
C281
) as a function
of: (a) radon weighted trajectory mean wind speed (0.6 m s
C281
bins), and (b) significant wave height (0.15 m bins). Whiskers show
the quartile range of the radon flux density with crosses indicating
the bin median and dots the bin mean. Solid lines represent a
quadratic fit a*x
2
C27c. Coefficients (variance) of the fit are for (a)
aC300.000580 (1.0293*10
C288
), cC300.0682 (0.000721), and for (b)
aC300.017 (2.798*10
C286
), cC300.00679 (0.000534).
OCEANIC RADON-222 FLUX 13
Mixing processes, which affect the composition of air
parcels, are not taken into account by our trajectory-based
analysis.
We attempted to isolate the effect of processes other
than flux variability on the radon flux distribution by com-
paring our radon concentration with output from several
transport models, which have all proscribed constant
oceanic radon fluxes. We chose to compare experimental
and model concentrations, rather than the corresponding
fluxes, as the experimental concentrations are measured
directly.
For this purpose, we used TransCom results for the
2002C12003 period (Law et al., 2008), selecting only those
hours (327) that were coincident with the Southern Ocean
selected hours from the observations (Set 7). This assumes
that the transport is sufficiently well modelled that the
temporal selection is filtering the model data in the same
way as the observations. Examination of the model time
series suggests that this is true, with only 1 hour when all
models exceed the observed concentration byC21100 mBq
m
C282
s
C281
. Since model radon concentrations were obtained
using a single proscribed oceanic flux (0.11 mBq m
C282
s
C281
),
broadening of the models? radon concentration distribu-
tions should be entirely attributable to their transport and
mixing processes. We obtain an estimate of the radon flux
variability by determining how much more the modelled
distributions need to be broadened (by convolution with
a normal or log-normal distribution) in order to match
observations.
Our initial selection of TransCom model output followed
that proposed by Law et al. (2010), whose choice was based
on a criterion of how well the models differentiated
between baseline and non-baseline periods at Cape Grim
(see Law et al., 2010 for details justifying the selection;
the list of selected models is in their Table 1). We further
refined the selection by requiring the models? median radon
concentrations to be within the 5thC195th percentile range
of our experimental radon concentration distribution.
This is important since the model?s distribution tends to
scale with the model?s median value so models with poorly
simulated median values could bias the assessment of
distribution broadening. Seven models fulfilled the condi-
tion. Experimental and model-average radon concentration
distributions are shown in Fig. 10. Contributing model
distributions are also shown to illustrate the differences
between the selected models.
Based on the experimental and model-average distribu-
tions in Fig. 10, we estimated the required model-average
distribution broadening to best match the experimental
distribution, using both normal and lognormal functions.
We found the former led to a far better fit with the
experiment; the RMS of residuals was 0.032 and 0.094 for
the normal (sC3011.3 mBq m
C283
) and log-normal (sC300.81
mBq m
C283
) broadening functions, respectively; RMS is
?50 ?48 ?46 ?44 ?42 ?40 ?38 ?36
Latitude (degrees)
0.0
0.2
0.4
0.6
0.8
?50 ?48 ?46 ?44 ?42 ?40 ?38 ?36
Latitude (degrees)
0.0
0.2
0.4
0.6
0.8
Radon flux density (mBq m
?2 
s
?1
)
Radon flux density (mBq m
?2 
s
?1
)
(b)
(a)
Fig. 9. Oceanic radon flux density (mBq m
C282
s
C281
) as a function
of latitude (18 bins) for (a) summer and (b) non-summer months.
Whiskers show the quartile range of the radon flux density with
crosses indicating the bin median and dots the bin mean. Solid
lines represent a linear fit. Coefficients of the fit are: (a) slopeC30
C280.00667, interceptC300.135; correlation coefficientC30C280.176,
significance levelC3099.9%, and (b) slopeC30 0.00111, interceptC30
0.314, correlation coefficientC300.034, significance levelC3055.3%.
Fig. 10. Comparison of radon concentration (mBq/SCM;
relative to the median) distributions for the least perturbed set in
2002C12003 obtained from experiment (E) and averaged model
(M); the contributing models? distributions are also shown
(1C30AM2.GFDL; 2C30AM2t.GFDL; 3C30CCAM.CSIRO;
4C30CCSR_NIES1.FRCGC; 5C30CCSR_NIES2.FRCGC;
6C30PCTM.CSU; 7C30TM3_vfg.BGC). For details regarding
the models see Law et al., 2008, 2010.
14 W. ZAHOROWSKI ET AL.
expressed as a fraction of the maximum frequency of the
experimental distribution. Based on this result we postulate
that the variability of the oceanic radon flux density,
integrated and weighted by radon decay along trajectories,
can be approximated by a normal distribution with sC30
0.075 mBq m
C282
s
C281
.
6. Conclusions
We have addressed the problem of representativeness of
spot measurements of oceanic radon flux densities, as
well as their scarcity and associated logistic and resource
problems, by proposing and testing an experimental
method for constraining radon flux density averaged over
large oceanic regions. The method was based on hourly
radon concentrations measured in marine air at Cape
Grim, a coastal baseline site, over the period 2001C12008.
The concentrations are selected to represent air parcels
least perturbed by land emissions and dilution by the
free troposphere, so that they can be assumed to be in
equilibrium with the oceanic radon source. The calculated
radon flux density distribution is representative for large
areas of the Southern Ocean, an important fetch area
for climate and air pollution studies in the Southern
Hemisphere.
The result compares well with the few available spot
measurements of radon flux density from the Southern
Ocean. Our flux estimates are close to some spot measure-
ments made in other oceanic regions; in some cases,
however, our results are significantly higher. We can only
assume that some spot measurements were made in calm
sea conditions and thus they tend to underestimate the
actual average flux density. In addition, our flux density
mean is significantly higher than most of those assumed for
modelling studies and for model comparison.
The obtained radon flux density estimates show a
significant seasonal dependence and, in summer, a latitu-
dinal dependence which has previously been independently
postulated. The flux density also depends on wind speed
and significant wave height. In both cases, this is consis-
tent with a quadratic dependence, as is the gas exchange
coefficient. The observed quadratic dependence validates
our choice of least perturbed concentrations as being in
equilibrium with the oceanic radon source.
We emphasise that the radon flux density distribution
we obtained should be interpreted as a distribution that
constrains the, likely narrower, actual flux distribution.
This is due to the fact that least perturbed radon con-
centration distributions are broadened not only by the
corresponding flux variability but also by a number of
mechanisms which air parcels are subjected to before
arriving at the measurement site. By comparing the least
perturbed radon concentration distribution obtained in
this study with a corresponding distribution of modelled
hourly observations averaged from seven TransCom mod-
els that all approximate the oceanic radon flux by a cons-
tant, the best agreement between the two is for a normally
distributed oceanic flux with sC300.075 mBq m
C282
s
C281
.
In future, a similar method could be applied to more
remote measurements with less land influence, such as those
we recently commenced at Macquarie Island, to further
constrain or confirm these estimates.
7. Acknowledgements
We thank the staff at the Cape Grim Baseline Air Pollution
Station in Tasmania as well as Ot Sisoutham and Adrian
Element at Australian Nuclear Science and Technology
Organisation for their support of the radon measurement
program at Cape Grim and Stuart Young and Graeme
Patterson for acquiring and pre-processing lidar data at
Cape Grim. We acknowledge the NOAA Air Resources
Laboratory (ARL) who made available the HYSPLIT
transport and dispersion model and the relevant input files
for the generation of back-trajectories used in this paper.
ECMWF ERA-Interim and sea ice data used in this
study were obtained from the ECMWF and NSIDC data
servers, respectively. The TransCom model simulations
were provided by AM2/AM2t (S. Fan, S.-J. Lin), CCAM
(R. M. Law), CCSR_NIES1/2 (P. Patra, M. Takigawa),
PCTM.CSU (R. Lokupitiya, A. S. Denning, N. Parazoo, J.
Kleist), TM3_vfg (C. Ro? denbeck). Instructions for acces-
sing the model output can be found at http://transcom.
project.asu.edu/T4_continuousSim.php. One of us (WZ) is
indebted to Stewart Whittlestone for an introduction to the
field of radon baseline observations.
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