A three-dimensional simulation of gravitational separation, defined as the
process of atmospheric molecule separation under gravity according to their
molar masses, is performed for the first time in the upper troposphere and
lower stratosphere. We analyze distributions of two isotopes with a small
difference in molecular mass (13C16O2 (Mi=45) and
12C16O2 (Mi=44)) simulated by the National Institute for
Environmental Studies (NIES) chemical transport model (TM) with a
parameterization of molecular diffusion. The NIES model employs global
reanalysis and an isentropic vertical coordinate and uses optimized
CO2 fluxes. The applicability of the NIES TM to the modeling of
gravitational separation is demonstrated by a comparison with measurements
recorded by high-precision cryogenic balloon-borne samplers in the lower
stratosphere. We investigate the processes affecting the seasonality of
gravitational separation and examine the age of air derived from the tracer
distributions modeled by the NIES TM. We find a strong relationship between
age of air and gravitational separation for the main climatic zones. The
advantages and limitations of using age of air and gravitational separation
as indicators of the variability in the stratosphere circulation are
discussed.
Introduction
proposed two different dynamical regimes: the homosphere
(perfect mixing) in the lower part of the atmosphere and the heterosphere
(diffusive equilibrium), where molecular diffusion leads to a separation of
the atmospheric constituents according to their molecular mass. The process
of atmospheric molecule separation according to their molar mass under
gravity is termed “gravitational separation” (GS). The GS of atmospheric
components is recognized as dominant in the atmosphere above a level of about
100 km called the turbopause . Recently, the existence of
GS of the major atmospheric components in the stratosphere was confirmed both
experimentally using a precise cryogenic sampler and theoretically by
two-dimensional numerical model simulations .
The Brewer–Dobson circulation (BDC) is the global circulation in the
stratosphere, consisting of air masses that rise across the tropical
tropopause and then move poleward and descend into the extratropical troposphere
. Currently, the “mean age of air” (mean AoA), defined
as the average time that an air parcel has spent in the stratosphere, is
perhaps best known for being a proxy for the rate of the stratospheric mean
meridional circulation and the whole BDC. The mean AoA provides an integrated
measure of the net effect of all transport mechanisms on the tracers and air
mass fluxes between the troposphere and stratosphere .
The increase in greenhouse gas abundance is leading to increased radiative
forcing and therefore to warming of the troposphere and cooling of the
stratosphere . A strengthened BDC under climate change in the
middle and lower stratosphere is robustly predicted by various
chemistry–climate models . However, this is not in agreement with
over 30 years of observations of the age of stratospheric air
.
The sparse limited observations in the upper troposphere and lower
stratosphere (UTLS) mean that chemical transport models (CTMs) are
complementary and useful tools for widely diagnosing the BDC and representing
the global transport and distribution of long-lived species. CTMs perform
relatively well in the UTLS despite resolution issues. Confidence is high in
the ability of models to reproduce many of the features, including the basic
dynamics of the stratospheric BDC and the tropospheric baroclinic general
circulation in the extratropics, the tropopause inversion layer, the
large-scale zonal mean, and tropical and extratropical tropopause
.
As future changes to the BDC are likely to be complex, a suite of methods,
parameters, and tools is necessary to detect these changes.
evaluated the capability of using seven trace gases to
estimate stratospheric mean ages. proposed using GS as
an indicator of changes in the atmospheric circulation in the stratosphere.
Analyses of GS, in addition to the CO2 and SF6 ages, may be useful
for providing information on stratospheric circulation.
also performed the first simulation of GS using the NCAR
two-dimensional (2-D) SOCRATES model (Simulation Of Chemistry, Radiation, and
Transport of Environmentally important Species, ), which
is an interactive chemical, dynamical, and radiative model. The spatial
domain of the model extends from the surface to 120 km in altitude. The
vertical and horizontal resolutions are 1 km and 5∘, respectively.
To extend the earlier work and overcome the inherent limitations of the 2-D
model, we here present a more quantitative analysis using the National
Institute for Environmental Studies (NIES) Eulerian three-dimensional (3-D)
transport model (TM). The remainder of this paper is organized as follows.
Overviews of the NIES TM, a method for modeling GS, and the simulation setup
are provided in Sect. . In Sect. we study modeled GS and
compare vertical profiles with those observed. Finally, a summary and
conclusions are provided in Sect. .
Model and method
For further investigation of the GS process we redesigned and modified the
NIES TM, which has previously been used to study the seasonal and
inter-annual variability of greenhouse gases (i.e., CO2 and CH4
by ).
Model
The NIES model is an offline transport model driven by the Japanese
Meteorological Agency Climate Data Assimilation System (JCDAS) datasets
. It employs a reduced horizontal latitude–longitude
grid with a spatial resolution of 2.5∘×2.5∘ near the
equator and a flexible hybrid sigma–isentropic
(σ–θ) vertical coordinate, which includes 32 levels from the
surface up to 5 hPa .
The model uses a revised version of hybrid isentropic grid. The original version
used isentropic levels above the 350 K potential temperature level and sigma
levels between the surface and 350 K level . A modified
hybrid isentropic grid was introduced to simulate better vertical transport
above the tropopause, extending the bottom level of the isentropic part to 295 K, as
first used in the NIES TM simulation for the age-of-air intercomparison study
. To limit application of the isentropic grid to the mid- to upper
troposphere, for each potential temperature level between 295 and 350 K, a
corresponding upper limit for pressure is set at fixed sigma level. For each
theta level in a list [295, 300, 305, 310, 315, 320, 330, 340], the upper sigma
limit is gradually changing from a sigma level of 0.6 for the 295 K level to 0.35
for the 340 K level, as [0.6, 0.54, 0.5, 0.47, 0.44, 0.41, 0.38, 0.35, 0.32].
For model levels between 295 and 340 K, once the sigma level reaches the
prescribed maximum for this model level, vertical transport switches from one
based on the diabatic heating rate to using vertical wind provided by reanalysis.
Over the isentropic part of the grid, the vertical transport follows the
seasonally varying climatological diabatic heating rate derived from
reanalysis.
Following the approach by , transport processes in the
planetary boundary layer (height provided by the ECMWF ERA-Interim
reanalysis; ) and in the free troposphere are separated with a turbulent
diffusivity parameterization. The modified cumulus convection parameterization
scheme computes the vertical mass fluxes in a cumulus cell using conservation
of moisture derived from a distribution of convective precipitation in the
reanalysis dataset . To set cloud top and
cloud bottom height a modified Kuo-type parameterization scheme
is used. Computation of entrainment and detrainment
processes accompanying the transport by convective updrafts and downdrafts is
as employed by .
Molecular diffusion
According to , assuming a neutral gas, the equation for the
vertical component of the diffusion velocity of gas 1 relative to gas 2 (w=w1-w2) in a binary mixture of gas 1 and gas 2 in a gravitational field
can be written as
w1-w2=-D12[n2n1n2∂(n1/n)∂z+M2-M1M1p∂p∂z+αTT∂T∂z],
where n1 and n2 are the concentrations of particles 1 and 2,
respectively; n=n1+n2 is the total concentration of the binary
mixture; T is the absolute neutral gas temperature; p is the total
pressure; M1 and M2 are the masses of particles 1 and 2; M=(n1M1+n2M2)/(n1+n2) is the mean molecular mass;
D12 is the molecular diffusion coefficient of gas 1 in gas 2; and
αT is the thermal diffusion factor. The three terms in the brackets
represent, from left to right, the effects of concentration gradient,
pressure gradient, and temperature gradient, respectively, on the molecular
diffusion velocity.
If component i is a multicomponent mix of minor constituents, then, using
the hydrostatic equation, the perfect gas law, and Eq. (), the
diffusion velocity wi for the ith minor constituent can be written in
time-independent form (for more details see pp. 33–34 in
):
wi=-Di[1ni∂ni∂z+1Hi+(1+αTi)1T∂T∂z],
where Hi=kT/Mig is the scale height, k=1.38×10-23 J K-1 is the Boltzmann constant, g=9.81 m s-2 is
the standard acceleration due to gravity for Earth, and Di is the
molecular diffusion coefficient. Here also ni and
αTi are
the number density and the thermal diffusion factor for species i,
respectively.
Similar to the SOCRATES model the vertical component of velocity is converted
to the vertical component of the molecular diffusion flux of a minor constituent
i relative to air :
fi=-Di[∂ni∂z+niHi+(1+αTi)niT∂T∂z].
The molecular diffusion coefficient is estimated from kinetic gas theory by
to be
Dicm2s-1=1.52×10181Mi+1M×Tn,
where Mi and M are the mass of the minor
constituent i and the mean molecular mass in atomic mass units,
respectively, and n is the number density of air.
To derive a diffusive flux formulation consistent with the NIES model
transport equation, the number density ni is substituted by the mixing
ratio Ci=ni/n:
fi=-Di×n×Ci[1Ci∂Ci∂z+(1Hi-1H)+αTi1T∂T∂z].
Here H is the atmospheric scale height, and
αTi is assumed to be zero
since the thermal diffusion effect would be of no importance in the
stratosphere . The derived flux was added to the standard
transport formulation for each species.
The eddy vertical diffusion in the stratosphere is often neglected in CTMs.
However, it should be considered along with molecular diffusion for modeling
GS. In the SOCRATES model two options are available for the parameterization
of the gravity wave forcing and vertical diffusion coefficient
. The default standard option uses the Lindzen–Holton
gravity wave breaking scheme . A second option
is to use the parameterization scheme developed primarily by
, which uses a general gravity wave spectral formulation
provided by the observed gravity wave spectrum to deduce how the gravity wave
energy flux responds to variation in the background environment. The first
scheme was adopted in the current NIES TM simulation. In general, the eddy
diffusion mixes concentrations in the volume, reduces vertical
stratification, and thereby weakens the molecular diffusion effect, as discussed by
.
Simulation setup
The study of GS requires considering two isotopes of atmospheric tracers with
a small difference in molecular mass. Following the setup for the SOCRATES
baseline-atmosphere run , we calculated the distributions
of 13C16O2 (Mi=45) and 12C16O2 (Mi=44). Firstly, a 20-year spin-up calculation with CO2 using
biospheric and oceanic fluxes only and then a 29-year (1988–2016)
simulation with total CO2 fluxes (biospheric, oceanic, and fossil
fuel) were performed. The fluxes used were obtained with the GELCA-EOF
(Global Eulerian–Lagrangian Coupled Atmospheric model with Empirical
Orthogonal Function) inverse modeling scheme . This set
of fluxes reproduces realistic time and spatial distributions of CO2
mixing ratio with strong seasonal variations in the Northern Hemisphere (NH)
and weak variations in the Southern Hemisphere (SH). The period 1988–2014 is
covered by the original JCDAS, which is extended by JRA-55 (an updated
version of the Japanese reanalysis) remapped to the same horizontal and
vertical grid, as JCDAS production discontinued after 2014. Use of JRA-55 for
the whole simulated period is preferable; however, model redevelopment is
required to take full advantage of the improved vertical and horizontal
resolutions.
The 〈δ〉 value, a measure of the GS, is defined as the isotopic
ratio of the CO2:
〈δ〉=δ13C16O2=[n13C16O2/n12C16O2]strat[n13C16O2/n12C16O2]trop-1,
where subscripts strat and trop denote the stratospheric
and tropospheric values, respectively. As the tropospheric value
trop we selected the model tracer concentration from the third
level, which corresponds to the lower boundary of the free troposphere. The
tropospheric 〈δ〉 variations are very small and
negligible compared with those in the stratosphere.
CO2 is a useful tracer of atmospheric dynamics and transport due to
its long lifetime. It is chemically inert in the free troposphere and has
only a small stratospheric source (up to 1 ppm) from methane oxidation
. Sufficiently accurate estimation of emissions and sinks,
together with knowledge of their trend in combination with the good
performance of the NIES model in simulating greenhouse gases, makes the
selection of CO2 appropriate for this study.
Results and discussionZonal mean distribution
The zonal mean distribution of CO2 in the upper part of the
atmosphere is driven by the large-scale transport processes: fast
quasi-isentropic mixing is combined with upwelling in the tropics and
downwelling in the extratropical lowermost stratosphere. In the troposphere,
vertical mixing is well developed. With height, the dynamic characteristics
weaken, and the mass flux due to molecular diffusion increases
(Eq. ). At a certain level near the tropopause, vertical mixing
is no longer able to suppress diffusion and the 〈δ〉
value becomes nonzero.
Annual mean altitude–latitude distributions of 〈δ〉 value (per meg) (a) and the AoA (years) (b)
calculated using the NIES TM. The unit (per meg) is typically used in isotope
analysis and is the same as 10-3 ‰.
Along with the 〈δ〉 value we analyze the AoA
(Fig. ). For this, we used the idealized linearly growing
“surface” tracer proposed in the age-of-air intercomparison project
. To fit with our analysis period we extended the original
simulation period (1988–2014) to 29 years (1988–2016) with a shorter
(10 years) spin-up, as less time is required to reach equilibrium for the AoA
analysis.
The processes creating the deformation of the zonal mean cross sections of
the GS and the AoA are similar here: the tropical upwelling pumps in
tropospheric air and stretches the parameter profile upward
(Fig. ). The NIES TM results shows strong subtropical mixing
barriers in both hemispheres compared with the SOCRATES model.
Mean altitude–latitude distributions of the difference between the
reciprocals of Hi and H (Li-1 [1 km-1], see text) for
(a) JJA, (b) SON, (c) DJF, and (d) MAM.
The results are averaged for 2000–2015.
Mean latitude–longitude distributions of 〈δ〉
value at the 10 hPa level in south polar and north polar projections for
(a) JJA, (b) SON, (c) DJF, and (d) MAM.
The results are averaged for 2000–2015.
Profiles of 〈δ〉 calculated by the model for
February, April, June, August, October, December, and yearly mean averaged
for 2000–2015 over the five latitudinal bands shown in Table
To estimate the contribution of atmospheric conditions to molecular
diffusion, we consider the sum of the three terms in the square bracket of
Eq. (). Because the contribution of the first term
(concentration gradient) is relatively small, the second term (originated
from pressure gradient in Eq. ) is the major contributor among
the three terms. Therefore, the sum of the three terms can be approximated by the
difference between the reciprocals of two scale heights (hereafter referred
to as Li-1). It has a dimension reciprocal to the length and is
interpreted as a measure of the efficiency of vertical molecular diffusion
under gravity. In view of the essentially one-dimensional nature of GS, it is
interesting to consider how Li-1 distributes in the troposphere and
stratosphere. Figure shows the latitude–height distribution
of Li-1 averaged in each season for the case of
12C16O2. Here the positive values indicate that
12C16O2 molecules descend relative to major constituents. The
temperature fields necessary for the calculation are taken from the JCDAS
reanalysis. Since Li-1 is inversely proportional to temperature, it is
generally small in the troposphere and has maxima in cold regions such as
the tropical tropopause region and the winter time stratosphere.
The enhancement of Li-1 does not readily result in a remarkable GS,
because it is the difference of Li-1 between 13C16O2
and 12C16O2 that creates GS in our case. We could expect that
the enhancement of Li-1 combined with the long stratospheric transit
time in the polar stratosphere will be favorable for GS. Figure 3 compares
the horizontal distributions of the seasonal mean 〈δ〉 on
10 hPa pressure surface in polar projections. We can see remarkable GS
(small values of 〈δ〉) in both polar regions exhibiting
surprisingly clear axial symmetry. In the present analysis, the physical
processes that drive GS (Eq. ) have been rearranged in the form
of Eq. () to separate the contribution to GS in two factors,
one the concentration gradient (the first term) and the other the temperature
structure. A stronger seasonal variability of GS in the Southern Hemisphere
is caused by changes in vertical pressure gradient (Eq. )
reflected to those in scale height difference between species.
Latitude bands used for averaging 〈δ〉 values.
NumberShort nameLong nameLatitude interval1SHLSouthern high latitudes90∘ S–60∘ S2SMLSouthern middle latitudes60∘ S–15∘ S3TPLTropical latitudes15∘ S–15∘ N4NMLNorthern middle latitudes15∘ N–60∘ N5NHLNorthern high latitudes60∘ N–90∘ N
To minimize local temporal and spatial effects, the seasonal variation of
vertical profiles was analyzed for five main climate zones: the southern and
northern high latitudes, the southern and northern middle latitudes, and the
tropics, as shown in Table and averaged over time (2000–2015).
It is clear from Fig. that seasonal variation is evident from a
level of about 12 km, except in the tropical region, where it starts from
20 km. The amplitude increases with height and reaches a maximum at the top
of the model domain. It is quite obvious that seasonal variability is almost
imperceptible in the tropics and increases towards the poles. The maximum and
minimum values are reached in December–February and June–August for the
South Pole and in April–June and October–December for the North Pole,
respectively.
Observation sites.
NumberSite nameSite coordinatesObservation dates1Biak, Indonesia(1∘ S, 136∘ E)22–28 Feb 20152Kiruna, Sweden(68∘ N, 21∘ E)18 Mar 19973Sanriku, Japan(39∘ N, 142∘ E)8 Jun 1995, 31 May 1999, 28 Aug 2000,30 May 2001, 4 Sep 2002, 6 Sep 2004,3 Jun 2006, and 4 Jun 20074Syowa, Antarctica(69∘ S, 39∘ E)3 Jan 1998 and 5 Jan 20045Taiki, Japan(43∘ N, 143∘ E)22 Aug 2010
Probably the stronger polar vortex in the SH presumably leads to the enhanced
GS (smaller values of 〈δ〉) in JJA in the SH
(Figs. a and a light blue line) compared to that
in DJF in the NH (Figs. c and e). In the NH, on
the other hand, GS appears weakest during winter (Fig. c) in
spite of the enhancement of Li-1 (Fig. c).
Weak sensitivity to seasonal changes of the tracer concentration is a
significant advantage of GS over AoA, which is the standard indicator of
circulation in the stratosphere. On the other hand, this method requires more
accurate sampling tools (i.e., balloon-borne cryogenic samplers) that are
more difficult to deploy than other more common methods.
Vertical profiles over Sanriku of 〈δ〉 (per meg)
(a, c) and AoA (years) (b, d) calculated from the modeled
“surface” tracer (in red) and the observed CO2 (in blue)
distributions for 4 June 2007 (a, b) and 28 August 2000 (c, d).
Same as Fig. but over the Kiruna site for
18 March 1997.
Vertical profiles
The modeled results are compared with measurements over the main climatic
zones: the circumpolar regions, the temperate latitudes, and the tropical
latitudes (Table ). The collection of stratospheric air samples
using a balloon-borne cryogenic sampler was initiated in 1985 at the Sanriku
Balloon Center of the Institute of Space and Astronautical Science
. The program has continued up to the present. In addition to
observations over Japan, stratospheric air samples were also collected over
the Scandinavian Peninsula, Antarctica, and Indonesia.
From those air samples, δ(15N) of N2,
δ(18O) of O2, δ(O2/N2),
δ(Ar/N2), and δ(40Ar) were derived to detect GS
in the stratosphere . The
effect of GS on the isotopic ratio depends on Δm rather than on the
atmospheric component, as follows from Eq. . Thus δ
values from observations of various tracers and the current simulations can
be compared.
Same as Fig. but over the Syowa station for
3 January 1998 (a) and 5 January 2004 (b).
Same as Fig. but over the Biak site for
22–28 February 2015.
Most of the observations were collected in the northern part of Japan over
Sanriku (eight profiles) and Taiki (one) in the warm season. Five profiles
were observed at the beginning of summer (late May to early June) and four
profiles at the end of summer (late August to early September). Typical
spring and fall profiles are shown in Fig. . For this
comparison, the modeled data are daily output at the nearest grid cell.
In the NH, the tropospheric CO2 is dominated by a strong seasonal
cycle due to biospheric activity, which removes CO2 by photosynthesis
during the growing phase to reach a minimum in August–September and releases
it during boreal autumn and winter with a maximum in April–May. Due to
steady growth and seasonal variation, CO2 concentrations in the
atmosphere contain both monotonically increasing and periodic signals. Spring
profiles are smoother, while in autumn they vary with height. The observed
AoA shows a strong inversion in the lower altitudes due to seasonal uptake of
CO2, as confirmed by CONTRAIL measurements and the Lagrangian transport model TRACZILLA .
The AoA calculated from the modeled surface tracer does not reproduce such
effect.
For the high-latitude sites Kiruna and Syowa
(Figs. –) the observed profiles are mainly
smooth and have smaller vertical fluctuation, apart from the uppermost level
over the Syowa station for 5 January 2004.
Due to the limited availability of balloon launching facilities, only one air
sample has been conducted in the equatorial mid-stratosphere over Biak
. The observed distribution can be explained by the mixing
of large-scale NH and SH background values and long-range tracer transport
with convective lifting . For this site, the 〈δ〉 value and the AoA variations with height are very small
(Fig. ), as vertical upwelling pumps young and well-mixed air
upward.
Although this is not a model validation paper, it is necessary to evaluate
the modeled results by comparison with observations, as the new
parameterization for GS simulation was incorporated in the NIES TM. A
detailed statistical analysis is summarized for the five observational sites
in Table . This includes the standard deviation of the misfit
between modeled and observed AoA and 〈δ〉
(σ(ΔAoA) and σ(Δ〈δ〉),
respectively), the Pearson correlation coefficient between modeled and
observed AoA and 〈δ〉 (r(AoA) and
r(〈δ〉), respectively), and the Pearson
correlation coefficient between AoA and 〈δ〉 from
observation (r(AoA, 〈δ〉)o) and from
the model (r(AoA, 〈δ〉)m). To
calculate standard deviation and correlation coefficients only coincident
points were selected.
The high values of the correlation coefficients between simulated and
observed parameters prove the model efficiency with the implemented
parameterization. The lower correlation for GS than for r(AoA)
stresses the complexity of the high-precision cryogenic sampling required for
GS. For example, most observed profiles show a tendency to have zones of very
weak increase or even inversion of the parameters starting from a level of
20–25 km. Despite these limitations, the ability to study the physics
underlying GS is a fundamental advantage of the 3-D simulation compared with
the 2-D simulation as performed by SOCRATES.
The standard deviations σ(AoA) and σ(〈δ〉)
quantify model–observation misfits. We stress a tendency of increase towards
the high latitudes, although it seems that a larger gap is obtained for Biak.
However, if we normalize the standard deviations by the value of the absolute
maximum value of the characteristic, the error decreases towards high
latitudes.
Standard deviation of misfit between modeled and observed AoA and
〈δ〉 value (σ(ΔAoA) and
σ(Δ〈δ〉), respectively); the Pearson
correlation coefficient between modeled and observed AoA and 〈δ〉 value (r(AoA) and r(〈δ〉),
respectively); the Pearson correlation coefficient between AoA and 〈δ〉 from observation (r(AoA, 〈δ〉)o) and from the model (r(AoA, 〈δ〉)m).
GS and AoA are useful indicators of atmospheric transport processes. Two
other correlations from Table (r(AoA, 〈δ〉)o and r(AoA, 〈δ〉)m) quantify their relationship. Since one value
increases with height and the other decreases, the correlation is negative.
Modeled results show stronger anti-correlation than that observed, probably
due to more straightforward connections in transport simulation; the
parameterization used may not take into account additional factors affecting
GS. We also do not exclude the influence of observational errors.
Relationship between age of air and 〈δ〉
Further study of the relationship between the AoA and the 〈δ〉 value is useful for understanding the atmospheric processes, as both
would be affected to some extent by changes in the stratospheric circulation.
For comparison the modeled AoA and 〈δ〉 values were
averaged over the same five broad-latitude bands as in Sect. (see
Table ). Along with the modeled values (solid lines) the
observed data are also depicted (symbols) in Fig. .
Observation sites are quite evenly distributed across the selected latitude
bands: Syowa in the southern high latitudes, Biak in the tropics, Sanriku and
Taiki in the northern middle latitudes, and Kiruna in the northern high
latitudes.
Figure shows a near-one-to-one relationship between AoA and
GS regardless of latitude and height. Although the model–observation
discrepancy is significant for all layers, the distributions of observations
show a similar pattern for the lower part of the stratosphere despite large
scatter. The positive 〈δ〉 values obtained from
observations are not reproducible according to the theory.
The 〈δ〉 value decreases rapidly for age values older
than 4 years, as the molecular diffusion coefficient increases with
increasing height due to its pressure dependence (Eq. ), which
causes the enhancement of gravitational separation with increasing height.
This mechanism does not affect AoA significantly in the stratosphere
. This emphasizes a
nonlinearity in the GS–AoA relationship in the stratosphere.
Another difference stressed the independence of the driving mechanisms of
AoA, and GS is the influence of the seasonal variation. For younger air
(0–2 years) the seasonal variability of AoA is maximal and falls with
altitude, while the 〈δ〉 variability increases
continuously upwards, as described in Sect. .
assumed that vertical upwelling in he tropical
tropopause layer (TTL) acts to weaken and downwelling in high latitudes acts
to strengthen the effect on the GS. To minimize the discrepancy between the
model-calculated (with SOCRATES) and observed 〈δ〉 values
over the equatorial region, a correction of the mean meridional circulation
and the horizontal eddy diffusivity was performed. However, that correction
cannot fully improve the simulation of the stratospheric circulation and
hence the reproducibility of AoA and GS by the SOCRATES model. In this study
the tracer simulation is based on reanalysis, which describes the atmospheric
circulation in sufficient detail. As part of the global balanced air
transport the upwelling and downwelling are limited by various constraints
including mass flux conservation. The resulting tracer field distribution
simultaneously describes AoA and GS, so both parameters can be used to
describe the structure of the global atmospheric circulation. However, the
limited set of observations and the limitations of the model do not yet allow
us to investigate this mechanism and determine the structure of the AoA–GS
relationship in more detail.
presented a consistent intercomparison of AoA
according to five modern reanalyses (ERA-Interim, JRA-55, MERRA, MERRA-2, and
CFSR) and found significant diversity in the distributions which were
obtained with the BASCOE (Belgian Assimilation System for Chemical
Observations) transport model, depending on the input reanalysis. They have
also found large disagreement between the five reanalyses with respect to the
long-term trends of AoA. Thus, an ambitious multi-reanalyses approach is
needed to distinguish what is robust in the current GS results from what is
not.
Plot of 〈δ〉 value against AoA. The modeled
results are averaged for the five latitudinal bands shown in
Table and over time for 2000–2015. Symbols represent all
available observations for Kiruna, Syowa, Sanriku, Taiki, and Biak sites.
Error bar values correspond to 1σ.
Conclusions
A three-dimensional simulation of GS in the UTLS zone is performed for the
first time using the NIES TM with a molecular diffusion parameterization. We
consider the 〈δ〉 values derived from the distribution of
two isotopes 12C16O2 and 13C16O2. The modeled
〈δ〉 values are compared to observations and the zonal
mean distributions from the two-dimensional SOCRATES model.
In comparison with the SOCRATES simulation, the NIES model has a number of
significant advantages: a three-dimensional tracer transport simulation
driven by global JCDAS reanalysis and a vertical coordinate with isentropic
levels. The model is optimized to run the greenhouse gas simulation, as confirmed
through various validation and multi-model inter-comparisons. The use of
optimized CO2 fluxes provided realistic tracer distribution and
seasonality. Along with these strengths, some weaknesses are also revealed:
coarse vertical resolution and the shallow top of the model domain.
The model-to-observation comparison shows that the model with this molecular
diffusion parameterization is able to reproduce the mean value and the number
of small-scale fluctuations recorded by high-precision cryogenic
balloon-borne observations in the lower stratosphere. This reconstruction
suggests that the tracer distribution can be explained by the properties of
transport, as resolved by meteorological reanalysis and the representation of
sub-grid-scale effects as diffusion. Overall, the implemented molecular
diffusion parameterization in the NIES TM shows reasonable performance.
We found a strong relationship between the modeled GS and AoA, which is the
main indicator of circulation in the stratosphere. However, in contrast to
AoA, the GS has a lower sensitivity to seasonal variability, which is a
significant issue in studies of atmospheric circulation. Thus, the modeled GS
characteristics can provide useful insights and complement AoA information to
give a more comprehensive evaluation of structure changes in the UTLS.
However, due to the simplified approach and parameterizations, the presented
simulation of the GS using the NIES model could not fully achieve the
potential of 3-D modeling. Modern reanalysis dataset and recently developed
transport models that effectively simulated the upper atmosphere can be employed
to address these issues. Since this work is the first in 3-D modeling of GS,
we believe this insight is useful for the scientific community working in the
field of the UTLS studies.
Code and data availability
Additional data requests should be addressed to Dmitry
Belikov (dmitry.belikov@ees.hokudai.ac.jp).
Author contributions
DB carried out the NIES model simulations and the data analysis. SS, SI and
FH contributed to the design of the analysis and theoretical description of
the GS process. SM prepared the reanalysis data and contributed code of the
NIES TM. SA, SM and TK provided the observational data. SS, SI, FH, SM, SA,
SM and TK provided helpful discussion and comments. DB wrote the manuscript
with contributions from all co-authors.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
We sincerely thank the balloon engineering group of the Institute of Space
and Astronautical Science JAXA for their cooperation in our stratospheric air
sampling. This work is partly supported by the Japan Society for Promotion of
Science, Grant-in-Aid for Scientific Research (S) 26220101. We thank
Masatomo Fujiwara, associated Professor at Hokkaido University for useful
comments regarding the GS studies.
Review statement
This paper was edited by Peter Haynes and reviewed by two
anonymous referees.
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