Atmos. Chem. Phys., 14, 7135–7147, 2014
www.atmos-chem-phys.net/14/7135/2014/
doi:10.5194/acp-14-7135-2014
© Author(s) 2014. CC Attribution 3.0 License.
Trajectory model simulations of ozone (O3) and carbon monoxide
(CO) in the lower stratosphere
T. Wang1 , W. J. Randel2 , A. E. Dessler1 , M. R. Schoeberl3 , and D. E. Kinnison2
1 Texas
A&M University, College Station, TX, USA
Center for Atmospheric Research (NCAR), Boulder, CO, USA
3 Science and Technology Corporation, Lanham, MD, USA
2 National
Correspondence to: T. Wang ([email protected])
Received: 15 January 2014 – Published in Atmos. Chem. Phys. Discuss.: 7 March 2014
Revised: 19 May 2014 – Accepted: 2 June 2014 – Published: 16 July 2014
Abstract. A domain-filling, forward trajectory model originally developed for simulating stratospheric water vapor is
used to simulate ozone (O3 ) and carbon monoxide (CO) in
the lower stratosphere. Trajectories are initialized in the upper troposphere, and the circulation is based on reanalysis
wind fields. In addition, chemical production and loss rates
along trajectories are included using calculations from the
Whole Atmosphere Community Climate Model (WACCM).
The trajectory model results show good overall agreement
with satellite observations from the Aura Microwave Limb
Sounder (MLS) and the Atmospheric Chemistry Experiment
Fourier Transform Spectrometer (ACE-FTS) in terms of spatial structure and seasonal variability. The trajectory model
results also agree well with the Eulerian WACCM simulations. Analysis of the simulated tracers shows that seasonal
variations in tropical upwelling exerts strong influence on O3
and CO in the tropical lower stratosphere, and the coupled
seasonal cycles provide a useful test of the transport simulations. Interannual variations in the tracers are also closely
coupled to changes in upwelling, and the trajectory model
can accurately capture and explain observed changes during
2005–2011. This demonstrates the importance of variability in tropical upwelling in forcing chemical changes in the
tropical lower stratosphere.
1 Introduction
The influx of water vapor (H2 O) to the stratosphere is largely
determined by the large-scale troposphere-to-stratosphere
transport in the tropics, during which air is dehydrated across
the cold tropical tropopause (e.g., Fueglistaler et al., 2009,
and references therein). Observations such as the entry mixing ratios (Dessler, 1998; Dessler et al., 2013), the coherent
relations between water vapor and temperature (Mote et al.,
1996), and the extensive cirrus clouds near the tropopause
(e.g., Winker and Trepte, 1998; Wang and Dessler, 2012)
all support this understanding. Back trajectory models have
provided detailed simulations of stratospheric H2 O (e.g.,
Fueglistaler et al., 2005). More recently, a newly designed
domain-filling forward trajectory model driven by reanalysis
wind and temperature has demonstrated success at simulating the transport of H2 O in the stratosphere (Schoeberl and
Dessler, 2011; Schoeberl et al., 2012, 2013). In this trajectory
model, winds determine the pathways of parcels and temperature determines the H2 O content through an idealized saturation calculation. This simple advection–condensation strategy yields reasonable results for H2 O in the stratosphere, although the detailed results depend on the wind and temperature fields utilized and assumptions regarding supersaturation
(Liu et al., 2010; Schoeberl et al., 2012).
Besides H2 O, ozone (O3 ) and carbon monoxide (CO) are
also important trace gases in the stratosphere linked to circulation, transport, and climate forcing (for O3 ). Ozone abundances in the stratosphere cover a wide dynamic range (∼ 10s
of ppbv to a few ppmv), and are influenced by a variety of
chemical and dynamical processes, including photochemical production and loss and large- and small-scale transport
(for example, deep convective lofting of boundary layer lowO3 air to the upper troposphere; e.g., Folkins et al., 2002).
CO behaves as a tropospheric source gas, originating from
natural and anthropogenic emissions, including combustion
Published by Copernicus Publications on behalf of the European Geosciences Union.
7136
T. Wang et al.: Trajectory model simulations of O3 and CO in the lower stratosphere
processes near the surface and oxidation of methane and
other hydrocarbons within the troposphere. The main sink of
CO is oxidation by the hydroxyl radical (OH) (Logan et al.,
1981). The CO photochemical lifetime of 2–3 months makes
it a useful tracer for transport studies in the troposphere and
lower stratosphere (e.g., Bowman, 2006; Schoeberl et al.,
2006, 2008).
The purpose of this work is to simulate O3 and CO in
the lower stratosphere using the domain-filling, forward trajectory model used previously for stratospheric H2 O. Trajectory modeling of O3 and CO can provide useful tests
for simplified understanding of transport and chemical processes in the stratosphere, and provide complementary information to the H2 O simulations (which are primarily constrained by tropopause temperatures). In addition to testing circulation and transport within the trajectory model,
the O3 and CO simulations can provide understanding of
mechanisms leading to observed chemical behavior, including transport history and pathways, seasonal and interannual variations, and relation to the stratospheric age of air
(Waugh and Hall, 2002). Ozone and CO are complementary tracers representing primarily stratospheric and tropospheric sources; both tracers exhibit strong gradients across
the tropopause, and they are often combined using tracer correlations to understand transport and chemical behavior in
the upper troposphere and lower stratosphere (UTLS) (e.g.,
Fischer et al., 2000). Furthermore, O3 and CO exhibit relatively large out-of-phase seasonal cycles in the tropical lower
stratosphere (Randel et al., 2007), and these coupled variations provide a sensitive test of the trajectory model simulations in this region.
2
2.1
Model description and data used
Trajectory model
The trajectory model used here follows the details described
in Schoeberl and Dessler (2011) and Schoeberl et al. (2012),
with trajectories calculated using the Bowman trajectory
code (Bowman, 1993; Bowman and Carrie, 2002; Bowman
et al., 2013). Because of the overly dispersive behavior of
kinematic trajectories (e.g., Schoeberl et al., 2003; Liu et al.,
2010; Ploeger et al., 2010; Schoeberl and Dessler, 2011), we
perform diabatic trajectories using isentropic coordinates, in
which the vertical velocity is the potential temperature tendency converted from the diabatic heating rates via the thermodynamic equation (Andrews et al., 1987).
The methodology for the trajectory simulations of O3 and
CO follows Schoeberl and Dessler (2011) and Schoeberl et
al. (2012), wherein the parcels are initialized at the 370 K
isentrope, below the tropical tropopause, using climatological O3 and CO from the Aura Microwave Limb Sounder
(MLS, monthly means averaged over 2005–2011) to provide
approximate entry level values in the upper troposphere. The
Atmos. Chem. Phys., 14, 7135–7147, 2014
370 K level is chosen as the initialization level because it is
above the level of zero net heating rates and parcels there
tend to ascend to the stratosphere (see Sect. 5). To account
for chemical changes along the trajectories, we use chemical production and loss rates output from the Whole Atmosphere Community Climate Model (WACCM). Specifically,
the O3 and CO concentration carried by each parcel is modified from the previous time step using the production and
loss frequencies calculated from WACCM.
For each day 1350 parcels are initiated on an equal area
grid covering 40◦ N–40◦ S latitude and advected forward in
time by reanalysis winds. At the end of each day, any parcels
that have descended below the 250 hPa level are removed
since in most cases they have re-entered the troposphere.
The upper boundary is chosen to be the ∼ 2200 K isentrope
(∼ 1 hPa or ∼ 50 km) to cover the entire stratosphere. Parcels
are initialized and added to the ensemble consecutively each
day and the combined set of parcels is then run forward.
This process is repeated over the entire integration period.
As more and more parcels are injected into the model, the
stratospheric domain is filled up with parcels – this is the
concept of domain-filling used in our model. The trajectory
model simulations are started in January 1990 and integrated
to the end of 2011; after 3–4 years the system reaches steady
state with approximately 1 million parcels in the domain. We
focus on analyzing the model results during 2005–2011, to
overlap the MLS and Atmospheric Chemistry Experiment
Fourier Transform Spectrometer (ACE-FTS) observations.
Upwelling across the tropical tropopause in the trajectory
model is determined by the reanalysis total diabatic heating rates (Qtot , hereinafter Q), which include heating due
to long-wave and short-wave radiation, moist physics, friction, gravity wave drags, etc. As shown below, our simulations of O3 and CO are sensitive to the imposed upwelling.
There is substantial uncertainty in the detailed magnitude and
spatial structure of Q, as seen in the differences among separate reanalysis results (Schoeberl et al., 2012; Randel and
Jensen, 2013; Wright and Fueglistaler, 2013). Figure 1 illustrates the differences in Q in the tropics (15◦ N–15◦ S)
based on several reanalysis data sets, highlighting large differences in the UTLS. Given this uncertainty, we tested
the sensitivity of our calculations to variations in the heating rates by comparing results based on the NASA Modern
Era Retrospective-Analysis for Research and Applications
(MERRA; Rienecker et al., 2011) and the ECMWF ERA Interim reanalysis (ERAi; Dee et al., 2011). Below we highlight the sensitivity of the resulting O3 and CO simulations
to these imposed circulations.
Changes in chemical concentrations in the trajectory
model are calculated using the chemical continuity equation
(e.g., Dessler, 2000):
[χ]current = [χ]previous + Pχ − LFχ · [χ ]previous · 1t. (1)
Here, χ represents either O3 or CO. Current chemical
concentrations [χ]current in volume mixing ratio (VMR)
www.atmos-chem-phys.net/14/7135/2014/
Vertical Profile:lon[0,360],lat[-90,90],vert[150,10],time[2000.042,2010.958],
X00: CFSR_monCat_latest_1979-2010.nc; X01: JRA25_anlyc_monCat_new_2000-2012.nc;
X02: MERRA_anlyc_monCat_new_1979-2012.nc; X03: ERAi_anlyc_monCat_new_1979-2012.nc;
CFSR
JRA25
MERRA
ERAi
Fig. 1
18o N-Smodel
average simulations of O3 and CO in the lower stratosphere
T. Wang
et al.:2000-2010,
Trajectory
7137
Fig. 2 with chemical lifetime superimposed, using the same color scale
N.Pole.[60,90]
LCO )/LCO
0
Pres (hPa)
0.1
0.2
0.2
Approx. Alt. (km)
-0.4
Alt
(km)
Approx. Alt.
(km)
Pres (hPa) Approx.
-
Approx.
Pres
(hPa) Alt (km)
Pres (hPa)
Pres. (hPa)
-0. -0.3
5
-0. -0. 0.7
97 9
Approx. Alt (km)
Pres (hPa)
0.0
-0.03
-0.01
1
3
Pres (hPa)
146.8
2.2
WACCM chemical production and loss rates
The chemical production and loss rates from WACCM4
(Garcia et al., 2007) are applied in our model to represent
chemical processes. The vertical domain of WACCM4 extends from the surface to the lower thermosphere, with horizontal resolution of 2.5◦ × 1.9◦ in longitude and latitude and
88 levels up to ∼ 150 km. In the UTLS the vertical resoluwww.atmos-chem-phys.net/14/7135/2014/
dency (production rate minus loss rate) divided by the loss
rate for Ox and CO. These show the mean differences of production to loss, with positive numbers indicating net chemical increase and negative numbers indicating net chemical
decrease; values close to zero imply balanced production
vs. loss. In the background are the respective photochemical
lifetimes evaluated from the loss rates (τL =[χ]/Lχ ). Both
Ox and CO exhibit relatively long lifetimes (> 3 months) in
the lower–middle stratosphere, and hence transport will have
a dominant influence on their distributions. Above ∼ 30 km
the lifetimes are shorter, and the photochemical behavior will
determine chemical structure.
Here Ox is the odd oxygen – the sum of O3 and O. We
use the production and loss rates of Ox because (1) the time
step of trajectory model is 1 day, which is much longer than
the lifetime of O3 in the middle and upper stratosphere, and
(2) the abundances of O3 only change on timescales comparable to or longer than the lifetime of Ox (see, e.g., Dessler,
2000, Chap. 3). Therefore, it is reasonable to use production
and loss rate of Ox instead of O3 for our purpose of simulation. Because O3 abundance is much greater than O – i.e.,
Ox ≈ O3 – throughout the rest of the paper we use O3 and Ox
interchangeably.
Ox production in the stratosphere is almost entirely due to
the photolysis of O2 . POx increases with altitude over most
of the stratosphere because the photolysis rate (proportional
Atmos. Chem. Phys., 14, 7135–7147, 2014
Pres (hPa)
Pres (hPa)
Approx.
Pres
(hPa) Alt (km)
Approx.
Pres
(hPa) Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Approx.
Approx. Alt
(km) Alt (km)
Pres (hPa)
are calculated from WACCM as a function of latitude, altitude, and climatological months. The time step for calculating trajectories is 45 min while the time step for calculating
chemical changes is 1 day.
One advantage of the Lagrangian framework is its ability
to trace the full evolution of parcels due to no explicit mixing
during the entire trajectory integration. However, there is an
effective “mixing” when many parcels are averaged within
grid boxes to be compared with either observational or Eulerian model results (see Sect. 3). The mixing in extratropical
tropopause is very important, but in this paper we mainly
focus our results on the tropical lower stratosphere, where
the strong vertical gradients of chemical species indicate less
mixing occurring. In fact, it is because Lagrangian models
producing nondiffusive transport and thus are especially accurate in regions where there are strong tracer gradients (e.g.,
the edge of polar vortex, the tropopause).
3yr
2yr
1yr
9mon
6mon
4mon
3mon
2mon
1mon
25day
20day
15day
10day
5day
1day
12hr
1hr
Approx. Alt (km)
20
48
45
42
40
37
34
32
29
26
24
21
18
16
13
20
Pres (hPa)
Pres. (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
WACCM
O3 (P-L)/L
fraction
WACCM
0.3 Ox Lifeime
0.01
0
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
-0.6
Approx. Alt (km)
Pres (hPa)
0.1
0
Approx. Alt (km)
-0.2
6
-0.
Approx. Alt (km) Approx. Alt (km)
Pres (hPa)
-0.1
0
0.1
Approx. Alt (km)
-0.2
-0.1
.1
24
3yr
100000
12yr 14.7
1yr
0.5
9mon21.5
0.3
6mon
0.2
4mon
0.1
03mon31.6
2mon
10.0
-0.1
1mon
-0.2
46.4
25day
14.7
-0.4
20day
-0.6
15day
21.5
68.1
-0.8
10day
-0.85
5day
31.6
-0.9
1day
100.0
-0.95
12hr
-1
1hr46.4
46.4
-90 -60
-30 0 30 46.4
60 90
30
-90 -60
-3031.6
0 Approx.
90 Alt.
20 60 68.1
(km)
68.1
68.1
-0.2 0.0 46.40.2 0.420 0.6 0.8
-0.246.4 0.0 0.2
0.4 0.6
-0.2
0.0 (O) 0.2 0.4 0.6 0.8
46.4
Lat.
Lat. (O)0.8
20
20
Lifetimes
(τ
)
L
17
17
17
68.1 46.4
68.1 46.4
68.1
46.4
Q
(K/day)
Q
(K/day)
Q
(K/day)
100.068.1
100.068.1
100.0
1hr 12hr 1day 5day 10day 15day
2mon 3mon 4mon 6mon 9mon 1yr 2yr 3yr
68.1
17 20
17 20
17 20day 25day 1mon
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
100.0 68.1
100.0 68.1
100.0
13 17
146.8
13 17
146.8
13
146.8
17
68.1
WACCM
CO 32
32
10.0averaged
32
10.0
10.0
Figure 1. Comparison
of 100.0
total diabatic heating
rates
over100.0 Figure 2. The146.8
100.0Lifetime
ratio-0.2
of chemical
(production
rate-0.6
minus
13 17
13 -0.0net tendency
146.8
-0.1 0.0 146.8
0.2 100.0
0.4 13
0.6 17
0.8
-0.5 -0.4 -0.3
-0.1
-0.9
-0.8 -0.7
-0.4 -0.3
◦
◦
100.0
100.0
(18 N–18146.8
S) in 2000–2010
from
four
different
re13 the deep tropics
13
146.8
13
146.8
loss rate
from14.7
WACCM
for (a)
and dTdt_tot
(b)
Nega(K/day)
dTdt_tot
(K/day)
14.7 -0.5 -0.4 -0.3 loss
14.7
-0.1 0.0 0.2 0.4 0.6 dTdt_tot
0.8
-0.2 rate)
-0.1 to
-0.0
-0.9
-0.8 -0.7
-0.6Ox -0.4
-0.3 CO.(K/day)
146.8ERA
13 28dTdt_tot
146.8-0.6 -0.4 -0.3
28 13
28net
data 0.4
sets:
MER
(MERRA),
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
(K/day)ERAi
(K/day)
dTdt_tot
(K/day)
-0.1analysis
0.0 146.8
0.2
0.6 dTdt_tot
0.8
-0.5 (ECMWF
-0.4 -0.3
-0.2 interim),
-0.1
-0.0
-0.9
-0.8
-0.7
tive numbers
are dashed
to highlight
the
chemical
decrease and
32 -0.2 -0.1 dTdt_tot
10.0 -0.8 -0.7
32 -0.6 -0.4 dTdt_tot
10.0
32
10.0
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
21.5
21.5
(K/day)
(K/day)
CFSRdTdt_tot
CFSR),
JRA25.
.0 0.2 0.4 0.6
0.8(NCEP
-0.5and
-0.4
-0.3
-0.021.5 (K/day) -0.9
-0.3indicate
positive
numbers
net chemical
increase, while10.0
zero lines
32
10.0
32
32
S.Pole.[-60,-90]
dTdt_tot (K/day) Tropical:Lat.[-30,30]
dTdt_tot (K/day) S.Mid.Lat.[-60,-30]
dTdt_tot (K/day) 10.0
14.7
14.7
14.7
24
24 10.0
indicate
and loss. For reference,
32
10.0 28 32
10.024 28
28 32 comparable amount
opical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
31.6of production
31.6
14.7 31.6
14.7
14.7
10.0 28 32
10.0 28 32 21.5
10.0 CO
21.5 are contoured in color.
21.5
the
respective
O
and
lifetimes
28
x
14.7
14.7
14.7
28 are determined
46.4 time 20
46.4
46.4
21.5 14.7
21.5
by concentrations
previous
step
24 28 in 21.5
24 28
14.7 20
14.7
20 24
31.6
31.6
31.6
21.5
21.5
21.5
28
28
24 the
24
24
[χ]previous
and
net
change,
derived
from
the
production
31.6 21.5
31.6 21.5
31.6
68.1
68.1 46.4
68.1
21.5
46.4
46.4
24
20
31.6
minus loss occurring
in31.6
each20time24 step.46.4
The production
rate31.6 20 24
17
17
17
24
24
46.4
46.4
31.6 20
31.6
31.6 The
20100.0
68.1
68.1
1.1–1.4 km.
WACCM4
simulation used here is
100.0 20 The 68.1
100.0
Pχ in VMR
per unit time
from WACCM.
loss46.4 tion is
46.4
46.4is obtained
20
20
17 20
17
17
68.1
68.1
68.1
run
with
specified
dynamics
(SD)
fields
(Lamarque
et
al.,
46.4
46.4
46.4
100.0
100.0
100.0
[χ]
in
VMR
per
unit
time
is
calculated
as
rate LF
·
13
146.8
13
146.8
13
146.8
20
20
χ 17 previous 68.1
68.1
68.1
17
17
2012),
in
which
the
model
is
nudged
by
the
MERRA
100.0
100.0 68.1
100.0meteo13
146.8
13
146.8
13
146.8
loss frequency
LF
(per
unit
time)
times
the
17 a product
17
17
68.1 of -0.2
68.1
χ
0.0 100.0
0.2 0.4
0.6 0.8
-0.2100.00.0 0.2 0.4 0.6 0.8
-0.2 0.0 0.2 0.4 0.6 0.8
rological
et al.,100.0
2011) from
January
2004
17
13 17
13
146.8(Rienecker
13 -0.0
146.8
-0.1representing
0.1 146.8
0.2 100.0
0.4
0.5 chemi0.6
-0.5
-0.4fields
-0.3
-0.2 -0.1
-1.1 -0.9
-0.6 to
-0.4 -0.2
chemical
concentration
(VMR),
a linear
Q (K/day)
Q (K/day)
Q-0.7
(K/day)
100.0
100.0
13
146.8
13
146.8
13
146.8
dTdt_tot
(K/day)
dTdt_tot
(K/day)
dTdt_tot
(K/day)
December
2011
(hereafter
we
only
use
WACCM
to
represent
-0.1 frequencies
0.1 0.2 0.4
0.5 0.6 from WACCM
-0.5 -0.4by -0.3 -0.2 -0.1 -0.0
-1.1 -0.9 -0.7 -0.6 -0.4 -0.2
cal loss.
The
loss
are
estimated
146.8 -0.2 -0.1
13 dTdt_tot
146.8-0.6 -0.4 -0.2
(K/day) -0.5 -0.4 -0.3
(K/day)
dTdt_tot (K/day)
-0.1 0.1 146.8
0.2 0.4 13
0.5 dTdt_tot
0.6
-0.0
-1.1 -0.9 -0.7
SD-WACCM4).
dividing
model
rate-0.3
Lχ -0.2
by the
concentradTdt_tot
(K/day)
dTdt_tot
(K/day)
(K/day)
.1 0.2 0.4 0.5
0.6 the
-0.5loss
-0.4
-0.1chemical
-0.0
-1.1 -0.9 -0.7 -0.6 -0.4 dTdt_tot
-0.2
Figure
2
shows
the
annual
zonal mean ratio of net tendTdt_tot (K/day) tion [χ], i.e., LFχ = Lχ /[χ].
dTdt_tot
In(K/day)
our simulation Pχ and LFχ dTdt_tot (K/day)
31.6
46.4 31.6
0 -0
24
-0 1 0
28
-0..1 0
2
13
4
24
-0.8-05 .8
21.5
05
-0.
-0.1
24
32
-0.3
17
.4
28
14.7
-0
10.0
28
1.0
1.5
2.2
3.2
4.6
6.8
10.0
14.7
21.5
31.6
46.4
68.1
100.0
146.8
32
[-15,0],[0,15]
20
32
[-15,0],[0,15]
0
24
-90 -60 -30 0 30 60 90
Lat. (O)
MER
ERAi
MER
10.0
48
45
42
40
37
4.6
37
.0
4mon
0
ERAi
CFSR
JRA25
0.01
24
24 3mon
[-15,0],[0,15]
N.Pole.[60,90]
31.6
31.6 -0.1 6.8
34
34
6.8 N.Mid.Lat.[30,60]
0
CFSR
JRA25
-010.0
2mon
.210.0
-0.01-0
32
10.0
32 10.0
32
32
32
[-15,0],[0,15]
N.Mid.Lat.[30,60]
N.Pole.[60,90]
1mon
-0.03 .4
29
29
10.0 46.4 32
10.0
3246.4 14.7
10.0
14.7
N.Mid.Lat.[30,60]
N.Pole.[60,90]
-0.05
25day
14.7
14.7
20
20
1
.
0
-0.1
21.5
26
2610.0
10.0 28 32
10.0
32
20day
00.1
28 21.5
28
N.Mid.Lat.[30,60]
N.Pole.[60,90]
0.2
14.7
14.7
14.7
-0.3
15day
31.6
24
24
31.6
10.0
10.0 2868.1 21.5
68.1 28 32 21.5 14.7
-0.5
14.7
14.7
10day
-0.7
46.4
21
21
46.4
0
28
21.5 14.7
21.5 14.7
21.5
5day
17
17
-0.
24 28
24
24
-0.9
-0.21 0
68.1
18
1821.5
68.1
-0.8
1day
100.0 24 28 31.6 21.5
100.0 31.6
-0.97 -0.85
21.5
24
12hr
100.0
16
100.0
-0.99
31.6 21.5
31.6 21.5
31.6-0.4 16
1hr
-1.0
46.4
146.8
13
1331.613 20
24
146.8
13 31.6 20 146.8
146.8 46.4
31.6 20 24
Pres (hPa)
a) (P – L )/L
WACCM CO Lifetime
WACCM CO (P-L)/L fraction
N.Mid.Lat.[30,60]
10.0
32
32
b) (PCO –
Ox
Ox 10.0
Ox
Vertical Profile:lon[0,360],lat[-90,90],vert[150,10],time[2000.042,2010.958],
X00: MERRA_anlyc_monCat_new_1979-2012.nc; X01: ERAi_anlyc_monCat_new_1979-2012.nc;
1.0
48
1.0
Vertical Profile:lon[0,360],lat[-90,90],vert[150,10],time[2000.042,2010.958],
X02: CFSR_monCat_latest_1979-2010.nc;
X03:
JRA25_anlyc_monCat_new_2000-2012.nc;
3yr
10
14.7
X00: MERRA_anlyc_monCat_new_1979-2012.nc; X01:14.7
ERAi_anlyc_monCat_new_1979-2012.nc;
1.5
45
1.5
6
Vertical Profile:lon[0,360],lat[-90,90],vert[150,10],time[2000.042,2010.958],
01
X02:28
CFSR_monCat_latest_1979-2010.nc; X03: JRA25_anlyc_monCat_new_2000-2012.nc;
28
28 2yr
5
-0.
1yr
X00: MERRA_anlyc_monCat_new_1979-2012.nc; X01: ERAi_anlyc_monCat_new_1979-2012.nc;
2.2
2.2
42
MER
ERAi
CFSR
JRA25
4
lon[0,360],lat[-90,90],vert[150,10],time[2000.042,2010.958],
X02: CFSR_monCat_latest_1979-2010.nc; X03: JRA25_anlyc_monCat_new_2000-2012.nc;
Pres.
21.5
21.5
3.2
40(hPa)9mon
3
3.2
anlyc_monCat_new_1979-2012.nc; X01: ERAi_anlyc_monCat_new_1979-2012.nc;
3
0.0
MER
ERAi
CFSR
JRA25
6mon
nCat_latest_1979-2010.nc; X03: JRA25_anlyc_monCat_new_2000-2012.nc;
1
5
4.6
4.6
37
Pres (hPa)
[-18,0],[0,18]
32
-60
-30
0
30
Lat. (O)
60
-60
-30
0
30
Lat. (O)
60
-60
-30
0
30
Lat. (O)
60
O3 (ppmv)
0.1
0.5
3.0
5.0
8.0
10.0
T. Wang et al.: Trajectory model simulations of O3 and CO in the lower stratosphere
Pres (hPa)
10.0
14.7
=800K
21.5
=700K
=600K
68.1
100.0
=500
=44
0K
-60
0.1
-30
0
30
Lat. (O)
0.5
=700K
=600K
=500
K
46.4
K
-30
1.5
0
30
Lat. (O)
O3 (ppmv)
3.0
24
=500
K
21
=44
18
16
0K
K
-60
26
=600K
=44
0
60
32
29
=800K
=800K
=700K
31.6
TRAJ_ERAiak
c) TRAJ_ERAi
WACak
b) WACCM
60
-60
5.0
-30
0
30
Lat. (O)
8.0
DJF
MLS
a) MLS
DJF
Approx. Alt. (km)
7138
1.5
60
10.0
Figure 3. Trajectory modeled O3 driven by ERAi reanalysis wind (c, TRAJ_ERAi) in boreal winter (DJF), compared to both MLS observations (a) and WACCM output (b). Both WACCM and trajectory model results are weighted by the MLS averaging kernels. Dashed lines
are potential temperature contours, demonstrating that in the lower stratosphere where transport dominates, chemical distributions roughly
follow the isentropes.
to solar radiation) increases faster with altitude than [O2 ] decreases, so the net effect is increasing. Figure 2a shows that
from 150 to ∼ 10 hPa Ox production exceeds loss in the tropics, whereas a net chemical decrease of O3 occurs in midto high latitudes (transport of O3 from the tropics to higher
latitudes closes the budget). Between about 10 and 2 hPa
the daily production and loss of Ox become comparable and
the system is in diurnal steady state. The system gradually
reaches photochemical steady states above ∼ 2 hPa, where
the lifetime of Ox is less than a day and the instantaneous
production and loss can be treated equally throughout the
day. Here we simply set the [Ox ] to be (POx /LFOx ) in our
trajectory calculations.
In the UTLS region, the main source of CO is from the
troposphere (from direct emissions and photochemical production), and this source is accounted for by initializing trajectories with observed values of CO. There is a net chemical
decrease (Fig. 2b) in the UTLS, where CO is predominantly
removed by oxidation with OH. CO experiences a net increase in the middle stratosphere (above ∼ 24 km) due to oxidation of methane (CH4 ), although this has little influence
on the results shown here.
In our simulation Pχ and LFχ are calculated from
WACCM as a function of latitude, altitude, and climatological months averaged over 2005–2011, so that a constant annual cycle is applied throughout the model integration. As
discussed in Sect. 5, this calculation will not accurately handle the situation where chemical losses are linked to meteorological behavior, such as for ozone losses in the polar winter
stratosphere.
2.3
and the observations at higher levels are used to evaluate
the trajectory model results. We use the MLS version 3.3
(v3.3) Level 2 products, described in the data quality and description document (http://mls.jpl.nasa.gov/data/v3-3_data_
quality_document.pdf). O3 profiles are available at 12 levels
per decade from 261 to 0.02 hPa, and CO profiles are available between 215 and 0.0046 hPa at 6 levels per decade. The
vertical resolution of O3 in the UTLS is approximately 2.5–
3 km while for CO it is ∼ 4.5–5 km. The detailed validation
for these data sets can be found in Froidevaux et al. (2008),
Pumphrey et al. (2007), and Livesey et al. (2008).
For the comparisons shown here, we applied the MLS
O3 and CO averaging kernels to both our trajectory simulations and WACCM model outputs when comparing with
the MLS observations. We have followed the detailed instructions for applying averaging kernels as described in http:
//mls.jpl.nasa.gov/data/v3-3_data_quality_document.pdf.
2.4
Other verifying data sets
Besides using chemical production and loss from WACCM,
we also compare O3 and CO modeled by WACCM to the trajectory model simulations, which serves as a sanity check
of applying the imposed WACCM chemistry, and also as
a simple comparison of Lagrangian vs. Eulerian model results. We also compare the trajectory modeling to the CO
measurements from ACE-FTS (Bernath et al., 2005), which
shows some systematic differences from MLS retrievals in
the stratosphere (Clerbaux et al., 2008; Park et al., 2013).
A detailed description of the ACE CO observations can be
found in Park et al. (2013).
MLS observations of O3 and CO
3
We will compare the model to observations of O3 and CO
from the Aura MLS (Waters et al., 2006). MLS climatology (constant annual cycle) of O3 and CO averaged from
August 2004 to December 2012 is used to set the initial
abundances when parcels are initialized at the 370 K level,
Atmos. Chem. Phys., 14, 7135–7147, 2014
3.1
Trajectory modeling results
O3 Results
Figure 3 shows the zonal mean cross section of O3 during December–February (DJF) from the trajectory model
www.atmos-chem-phys.net/14/7135/2014/
Vertical
Profile:lon[0,360],lat[-90,90],vert[150,10],time[2005.042,2011.958],
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
X02:
X02: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_3isob_mlsAK_monC
X02:130725_s100_i370_mthd_ERAi_day
130725_s100_i370_mthd_ERAi_d
X00:
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i370_mthd_MER_day_Wa
X00:
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_aver_isob2_mlsAK_monCat_2005-2011_150-1.nc;
X01:
130721_s100_i370_m
X00:
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i370_mthd_MER_day_Wac
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_2isob_var1_monCat_2005-2011.nc;
X03: mo
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_3isob_mlsAK_monCat_2005-2011_100-1.nc
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_2isob_var1_monCat_2005-2011.nc;
WACak
TRAJ_MERak
TRAJ_ERAiak WACak
MLSX03: mont
Fig. 4
WACak
2006 2
WACCM
WACak
WACCM
2005-2011,
18o N-S averageTRAJ_ERAiak
TRAJ_MER
TRAJ_ERAi
TRAJ_MERak
TRAJ_MER
TRAJ_ERAi
MLS
MLS
68 hPa
[-18,0],[0,18]
[-18,0],[0,18]
[-18,0],[0,18]
N.Mid.Lat.[30,60]
N.Mid.Lat.[30,60]
N.Mid.Lat.[30,60]
O3 Anom. (ppmv)
O3 Anom. (ppmv)
Pres
(hPa)
Pres
O
3 (ppmv)
Approx.
Alt (km)
O3 (ppmv)
Approx.
Approx. Alt (km)
T. Wang et al.: Trajectory model simulations of O3 and CO in the lower stratosphere
7139
05.042,2011.958],
0.8
ord_200501-201112_aver_isob2.nc;
X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_m
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
acl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03:
monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
2006
2008
X00: WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01: 2009
130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long
2006 2007 2008
2010 2011
2006
20
2006 2007
2007
2008
cal Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
X02: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03: monthly_gridded_mlsO3_noICE_mo
0.632
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1
32
10.0
32
90,90],vert[100,10],time[2005.042,2011.958],
32
10.0
32
10.0
32 a) MLS
10.0
10.0
32 68 hPa
b)
ER
TRAJ_ERAi
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03: monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
0.15
68
hPa
6832
hPa
68
hPa
m_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_m
1.0
1.0 0.10
ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
WACCM
TRAJ_MER 1.0X03:TRAJ_ERAi
MLS
0.4
N.
N.
N.P
Pres (hPa)
Pres (hPa)
3
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
O3 (ppmv)
(ppmv)
Approx. Alt (km)
O
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
3
Approx. Alt
Alt (km)
(km)
Approx.
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
O3 (ppmv)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)Pres (hPa)
Pres
O (hPa)
(ppmv)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Approx.
(km)
Approx. Alt
(km) Alt
Approx.
Alt (km)
Pres (hPa) Approx.
Alt (km)
Approx. Alt (km)
O3 (ppmv)
(ppmv)
O
3
14.7
14.7
14.7
14.70.8 0.0529
TRAJ_ERAi
14.7
14.7
29
28
0.8 MLS
0.8
29
2928
N.Pole.[60,90]
MLS
0.00
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
21.5
21.5
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
[-18,0],[0,18]
N.Mid.Lat.[30,60]
100 hPa
32 WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
10.0
32 X00: WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
10.0
0.6
0.6 N.Pole.[60,90]
0.25 X01:
130721_
21.5Vertical
21.50.6
X00:
X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcin
-0.05
Vertical Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
21.5
21.5
322425
10.0
32
10.0
32
[-18,0],[0,18]
N.Mid.Lat.[30,60]
N.Pole.[60,90]
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005
X02: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03:
monthly_gridded_mlsO3_noICE_monCat_2004
24
24
X00:
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i370_mthd_MER_da
25
25
X00:
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X
31.6 X02:
31.60.4
25
25
25
0.4 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_m
0.4 -0.10
0,18]
N.Mid.Lat.[30,60]
N.Pole.[60,90]
14.7 32 29
14.7 32
32 29
10.0
10.0
10.0
0.20
X02: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03: monthly_g
31.6 WACCM
31.6
14.7 10.0 31.6
10.0 WACCM
10.0
32
TRAJ_ERAi
MLS
TRAJ_MER 14.7 31.6
TRAJ_ERAi
MLS TRAJ_MER
29 32
29 100
29
0.35
hPa
0.35
0.35 100
hPa 14.7
100 hPa
100
hPa
0.15
46.4
46.4
14.7
14.7
0.04
WACCM
TRAJ_MER
TRAJ_ERAi
MLS
WACCM
TRAJ_MER
TRAJ_ERAi
21.5 29
21.5 29
20
20
20
22
22
22
29
0.30
22
22
0.30
0.30
14.7 22 29
25
2521.5 14.7
21.5 14.7 46.4
46.4
29
0.02
0.10
46.4
46.4
0.25
68.1
68.1
0.25
0.25
N.Mid.Lat.[30,60]
25
25 [-18,0],[0,18]
25N.Pole.[60,90]
[-18,0],[0,18]
N.Mid.Lat.[30,60]
21.5 31.6
21.5 31.6
21.5
0.20
N.Mid.Lat.[30,60]
[-18,0],[0,18]
N.Mid.Lat.[30
10.0 31.632 21.5
32
25
25
0.20 0.00
1719
17
17
32
10.025[-18,0],[0,18]
32 32
10.0
10.
21.5
21.5 0.20
19
31.6
19 25
19
1919 32
22
22
68.1
68.1
100.0
100.0
25
32
10.0
32
10.0
32
10.0
32
68.1
68.1
0.15
0.15
2006
2
0.15
-0.02
31.6
31.6
31.6
~370K
O3 initial46.4
46.4
22
22
22
WACCM 2829
TRAJ_MER
29
N.Mid.Lat.[30,60]
TRAJ_MER
TRAJ_ERAi
MLS and WACCM data. Note that the MERRA and ERAi
simulations use identical O3 initial values at 370 K, so that
the differences in Fig. 4a are primarily a result of differences in upward circulation (Fig. 1). The mean differences
in ozone in the lower stratosphere can be explained as a result of the different heating rates (vertical circulations) imposed. The ERAi heating rates are higher than MERRA up
to 20 km (Fig. 1). Due to the positive vertical gradient in O3
the stronger circulation moves air with lower O3 upward, creating a lower relative concentration compared to MERRA.
www.atmos-chem-phys.net/14/7135/2014/
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Pres
Pres(hPa)
(hPa)
Approx. Alt (km)
Approx.
Approx.Alt
Alt(km)
(km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx.
Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Pres (hPa)
Approx.
Alt (km)
Approx. Alt (km)
Pres (hPa)
Approx.Alt
Alt(km)
(km)
Approx.
Pres (hPa)
Pres (hPa)
Approx. Alt
(km)
Approx.
Alt (km)
Approx. Alt (km)
Approx. Alt (km) Pres (hPa) Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
14.7
value
31.6
0.10
46.4
29 29
1316
146.8
22
100.0
16 22
100.0
19
29
46.42968.1
19
0.2
0.5
1.3
3.6
10.0
21.5
46.4 19 0.1
46.4
0.1
0.3
0.7
1.7
4.3
11.0
0.1 0.3 0.7
1.7 19 4.3 68.1
11.0
OO3100.0
(ppmv)
16 25 25
(ppmv)
68.1
3
O
(ppmv)
19 Tropical:Lat.[-30,30]
25
325
31.6
Approx.
Alt (km)
Approx.
Alt (km)
Approx.
Alt (km)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
29
19
Approx. Alt (km)
14.714.7 46.429 31.6
14.
0.10
0.10 -0.0413
29
13
146.8
22
16
100.0
16
14.7
14.7
14.7
16
100.029
16
46.4 29
46.4 29
22
68.1
2006
2007
2008
20060.1
2007
2008
2006
2007
2008
2009
20103.6
2011
2006
20
19
0.1
0.2
0.5
10.0
0.1
0.2
21.51.3
21.5
21.
46.4
0.1
0.3
0.7
1.7
4.3
11.0
0.3
68.1
0.1 0.3 0.7
1.7
4.3
11.0
0.1
0.3
TiT0
25
Time
25
19 1625
19
21.5
21.5
21.5
O
33 (ppmv)
O(ppmv)
(ppmv)
68.1 100.0
68.1
O
O
25 331.631.6 100.0 68.1 16 25
25 S.P
S.Mid.Lat.[-60,-30]
100.0 68.1 16 19
31.6
31.
68.1 16
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.P
0.1 0.3
1.7Tropical:Lat.[-30,30]
11.0profile and
0.1 series
0.3(1000.7
1.7
4.3100.0
11.0
S.Mid.Lat.[-60,-30]
Figure0.7
4.32
Tropical
(a)4.3
vertical
hPa, bottom
panel;
68
hPa, upper
panel) of MLS,
and 31.6
trajectory
31.6
31.622WACCM,
16
100.0
16 (b) time
16
100.0
10.0
32
10.0
32 1.7 S.Po
22
22
22 10.0
22
32 0.3by MERRA
10.0Oover
32 tropics
10.0
32
◦ N–184.3
◦ S) from
0.1
0.7 1.7
4.3ERAi
11.0
0.3
0.7 (181.7
11.02005
0.1WACCM
0.3 32
0.7
4.3
O32
(ppmv)
(ppmv)
modeled
wind and
wind,
averaged
the
deep
to
2011.
Both
and
3O
3 0.1
32
10.0
3 driven
100.0
16
100.0
16
100.0
46.4
46.4
46.4
46.
22
22
22
22
22
O
(ppmv)
O
(ppmv)
O
(ppmv)
0.1 0.3 S.Mid.Lat.[-60,-30]
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
S.Pole.[-60,-90]
MERx1.05
trajectory model results are3 weighted by the MLS averaging
(a) the x axis
is in log scale to highlight the differences:
purple3
3
14.7kernels. In46.4
14.7 the
46.4
46.4
.7 32
4.3 11.0
0.1initial0.3
0.7 carried
1.7 by4.3
11.0
0.1
0.3into 0.7
1.7 at 370
4.3 K; 11.0
O
O14.7
14.7in red S.Pole.[-60,-90]
S.Mid.Lat.[-60,-30]
10.0
32
10.0
2829 Tropical:Lat.[-30,30]
28
28
square
indicates
the
O3 values
parcels
when
they were first
injected
the domain
the O
vertical
bars
indicate
3 (ppmv)
3 (ppmv)
3 (ppmv)
14.7
19
19
29
29
19
19 14.7
19
29
29
68.1
v)
O
O
(ppmv)
68.1
68.1
68.
32
10.0
32
10.0
32
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
the MLS29
vertical resolutions at
each
of
the
MLS
retrieval
pressure
levels.
3 (ppmv)
3
21.5 19
21.5
19
19
19
19 68.1
68.1 10.0
68.1
[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
14.7
14.7
32
10.0
32
32
10.0
21.5
21.5
2916
29 16 21.5
100.0 14.716 10.0
16 21.5
100.0
100.0
100
24
14.7 16
10.0 292425 32
10.0
32 24
31.6
31.6
25
25
29
29
25ERAi reanalysis
25
25
16
100.0
16
100.0
16
16
100.0
16
14.7
14.7
14.7
driven
by
(denoted
as
“TRAJ_ERAi”),
comMonthly
time
series
of
O
at
100
and
68
hPa
averaged
over
21.5
0.1
0.1 0.3
0.3 0.70.7 1.71.7 4.34.311.0
11.
29
29 3
0.1 14.7
0.3 0.7 1.7 4.3 21.5
11.0 29
0.114.7
0.3 0.30.7 0.71.7 1.7 4.3 4.3
11.011.0
0.1
31.6
31.6
◦ N–18◦ S)21.5
14.7 31.6
25
21.51.7
pared to O
both
the MLS observations
and the25
WACCM
results.
the(ppmv)
deep
tropics
(18
are shown
in4.3
Fig. O
4b.
There
is 0.7 0.1
31.6
29
29
46.4
46.4
0.1
0.3
0.7
4.3
11.0
0.1
0.3
0.7
1.7
11.0
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
1.7
O
O
(ppmv)
(ppmv)
O
(ppmv)
(ppmv)
3
3 3
20
2520
25 3 a320
25 related
21.5
Because
O22
hPa is31.6
in photochemical
steadyS.Mid.Lat.[-60,-30]
state
strong
annual
cycle in ozone
at these levels
to the
22 21.5
22 O3 (ppmv)
31.6
3 above 1021.5
O3 (ppmv)
O3 (ppmv)
O3 (ppmv)
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
Tropical:Lat.[-30,30]
S.Pole.[-60,-90]
22
22
22
25
25 Overall
21.5
21.5
21.5 68.1
46.4
46.4 et al., 2007;
(Sect. 2) we focus on O3 below 10
hPa.
seasonal
variations10.0
in25
tropical
upwelling
68.1
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,
32trajectory
10.0 31.6
10.0
32the46.4
32 32 (Randel
10.
2232
2231.6
46.4
25 with31.6
25Abalos
31.6
simulations
agree
results
from
both
MLS
and
WACCM.
et
al.,
2012,
2013a),
and
this
behavior
46.4
3246.4
32
10.0 31.6
10.0
32reproduced
17
17 32
2217
2232 10.0
22 is
19 stratosphere transport
19
31.6
31.6
31.6 100.0
In the 19
lower
plays an46.4
important
by19
the trajectory model,
agreement19
in
100.0role 19
46.4 reasonable
14.7showing
14.722
14.7
14.
22
22
68.1
29 68.1 of
29
29 29 and
68.1
68.1
1929
19
in re-distributing
chemical
species,
resulting
in
contours
amplitude
with
the
MLS
observations
WACCM
results
46.4
46.4
46.4
22
22
14.7
14.7
14.7
68.1
68.1
29
29
146.8O
13spite of the29differences in mean values).
146.8
13 29
1913
1929 (in
19
46.4
46.4
O3 approximately
following the21.5
isentropes. The
There are some68.1enhanced
16
100.0 3
16
100.0
16
21.5 68.1 46.4 100.0
21.5
21.
16
100.0
16
16
19 16
19
19
100.0
16hPa)
100.00.5 in 1.3
what0.1
larger
differences
annual25
cycle
amplitude
at21.5
68 hPa,
production 0.1
due to photolysis
at 301.3
km (∼ 3.6
10
shifts from
68.10.5
68.10.2
68.10.1
25
25
25
25
0.2
10.0
3.6
10.0
0.2
21.5
21.5
19
0.3 0.7
0.7
1.7 4.3
11.0
0.1
0.3
0.7(ppmv)
1.7100.0
11.0
0.1 250.3
0.3 0
160.10.1
100.0
1625 19with
16
68.1
68.1 fol68.1
south during
DJF0.3
towards
north
during
JJA4.3
(not
shown),
the 0.3
ERAi
showing
better
25
25 trajectory
25 agreement0.1
O
O
1.7
11.0
0.7
1.7results
4.34.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.70.14.3
11.0
3 (ppmv)
3 31.6
31.6
31.6
31.
16
100.0
16
100.0
16
100.0
O
(ppmv)
O
(ppmv)
with
MLS.
lowingOthe
seasonal
variations
of
photolysis
rates.
3
34.3
O3 (ppmv)
0.1 16 0.3 0.7
1.7
4.3 11.022 22
0.3 0.7 1.7O3 (ppmv)
11.0 22
0.1 31.6
0.3 0.7 1.7 4.3O
31.6
31.6
O3 0.1
(ppmv)
3 (ppmv)
22
22
100.0
100.0
16
100.0
The simulated
and observed
ofOO(ppmv)
the deep tropics
3 averaged over
O3 (ppmv)
O3 (ppmv)
0.1 0.3 0.7Vertical
1.7 profiles
4.3 11.0
0.1 0.3latitudinal
0.7 structure
1.7 224.3of zonal
11.0
3 22
46.4 0.1 0.3 0.7 1.722 4.3 11.0
46.446.4
22 lower
22 46.
◦ N–18◦ S) from 2005
mean
O
in
the
stratosphere
(68
hPa)
throughout
the
(18
to
2011
are
shown
in
Fig.
4a
3
.7 4.3 11.0
0.1 0.3 0.7 1.7 4.3 11.0
0.1 0.3
0.7 1.7 4.3 11.0
O3 (ppmv)
O3 (ppmv) 46.4
O
(ppmv)
46.4
46.4 3
seasonal cycleOis
shown in Fig. 5.19The
(note that the x axis is in log
scale). The trajectory
v)
O3 (ppmv)
19 overall variations are
19
19 19 model
3 (ppmv)
68.1
68.168.1 by the trajectory model, although
68.
driven by MERRA (denoted
as
“TRAJ_MER”)
shows
reareasonably
well
simulated
19
19
19
19 68.1
19 68.1
68.1
sonable agreement with MLS data (and WACCM)
in the
low biases are found compared
100.0 to16MLS
16 over middle-to-high
16
100.0
16 16
100.0
100
lower stratosphere, while the
results
based
on
ERAi
(denoted
latitudes
in
both
hemispheres
(and
WACCM 16
is systemati16
16
100.0
16
16 100.0
100.0
1.7 4.3over
4.311.0
11.
0.1 as0.3
0.7 1.7 show
4.3relatively
11.0 smaller O3 values.
0.1 0.1Above
0.3 0.30.7 0.7
1.7higher
0.10.1 0.30.3
1.71.7 4.34.311.0
“TRAJ_ERAi”)
cally
the11.0
globe). The development
of 0.7
the0.7
Antarc0.3 dominate,
0.7 1.7different
4.3 O11.0
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.7
O
(ppmv)
O
(ppmv)
O3 (ppmv)
(ppmv)
O
(ppmv)
24 km where
photochemical0.1
processes
tic
ozone
hole
is
evident
in
the
very
low
ozone
values
pole3
3
3
3
O3 both
(ppmv)
O3 (ppmv)
O3◦ (ppmv)
trajectory runs yield similar results and they
agree with
wards of 60
S in October, which
is simulated in the trajec- O3 (ppmv)
22
tory model based on the strong chemical ozone losses in this
region derived from WACCM.
Figure 6 compares the horizontal structure of boreal summer (JJA) O3 at 83 hPa from MLS data and the trajectory
results driven by MERRA. The trajectory results show a reasonable simulation of the spatial patterns compared to MLS
(and WACCM; not shown), with a clear minimum inside the
Asian monsoon anticyclone linked to upward transport of
ozone-poor air from lower levels (Park et al., 2009). There
Atmos. Chem. Phys., 14, 7135–7147, 2014
1.0
0.8
7140
15
0.6
-15
0.3
0.2
T. Wang et al.: Trajectory model 0simulations of O3 and CO in the lower stratosphere
0.4
68 hPa
0
O3 (ppmv) O3 (ppmv) O3 (ppmv) O3 (ppmv)
30 0
-90
1.5
-90
1.0
0.5
-60
-30
-60
0
60
60
30 90
90
60
90
JAN
JAN
JAN
APR
JUL
JUL
JUL
b) TRAJ_MER
1.0
0.8
15
0.6
0
0.4
-15
0.3
0.2
-30
APR
APR
a) MLS
30
0
60
120
180 240
Lon. (O)
300
360 0
60
120
180 240
Lon. (O)
300
360
Figure 6. Northern Hemisphere summertime (JJA) tropical O3 distributions at 83 hPa averaged from 2005 to 2011 between MLS and
JJA average
at 83 hPa(weighted by the MLS
the MERRA-driven2005-2011
trajectory
simulations
averaging kernels). Horizontal wind vectors from the MERRA reanalysis are overlaid to emphasize the Asian summer monsoon anticyclone.
OCT OCT
30
-30
Lat. ( ) 0 O
Lat. ( )
O
30
-30 hPa)
JJA (83
Lat. (O)
-90
-60
-30
68 hPa
68 hPa
2.5
-30
-90 -90-60 -60
-30
0
2.5 2.0
2.5
1.5
2.0
2.0 MLS
1.5 1.0
WACCM
MLS
1.5
1.0 0.5
TRAJ_MER
MLS
WACCM
0.5 1.0
TRAJ_ERAi
TRAJ_MER
WACCM
2.5
0.5
2.5 2.0TRAJ_ERAi
TRAJ_MER
2.0 1.5
2.5 TRAJ_ERAi
1.5 1.0
2.0
1.0 0.5
0.5 1.5
2.5
2.5 2.0
1.0
2.0 0.5
1.5 1.5
2.5
1.0 1.0
2.0
0.5 0.5
1.5
2.5 2.5
2.0 2.0
1.0
1.5 1.5
1.0 0.5
0.5 1.0
2.5
0.5
2.0
O3 (ppmv)
O3 (ppmv)
O3 (ppmv)
O3 (ppmv)
O3 (ppmv)
O3 (ppmv)
O3 (ppmv)
O3 (ppmv)
Fig. 5
100
30
O3 (ppmv)
Lat. (O)
Fig. 6
83
0.3
0.2
O3 (ppmv)
-30
-15
60
30
90
60
90
OCT
Figure 5. Zonal
of O3 at-30
68 hPa during January
(JAN),
-90mean -60
30
60 April
90
2005-2011
average at 68 hPa,0 ALL
original
O
(APR), July (JUL), and October (OCT)
in 2005–2011
Lat. (averaged
)
from MLS, WACCM, and trajectory results driven by MERRA
wind and ERAi wind. Both WACCM and trajectory model results
are weighted by the MLS averaging kernels. In October (Antarctic
spring time), the South Pole undergoes exceptional depletion of O3 .
is also relatively low O3 centered near 15◦ S linked to the
slow ascending air from the troposphere in this region.
The trajectory model is also able to capture the spatial
behavior of polar ozone. Figure 7 shows a comparison of
high-latitude O3 in the Northern Hemisphere (NH) during
winter (DJF) and in the Southern Hemisphere (SH) during
spring (September) between MLS and trajectory modeling
driven by both MERRA and ERAi winds. During NH winter, O3 -rich air (> 2.2 ppmv) occurs within the polar vortex
(denoted with the 24 PVU isopleth in Fig. 7a–c), and the trajectory model captures the observed isolation from middle
latitudes, although differences in magnitude exist. During
SH springtime, the Antarctic ozone hole (denoted with the
195 K isotherm in Fig. 7d–f) is reasonably well reproduced
in the trajectory model based on imposed WACCM chemistry. The trajectory model also captures the spatial structure of the zonal wave ozone maximum near 50◦ S (the socalled “ozone croissant”), linked to the descending branch
of the Brewer–Dobson (BD) circulation. The weaker extravortex high in ozone in the trajectory model may be related
to the weaker overall circulation in MERRA compared to
observations (Schoeberl et al., 2012, 2013). Noted that due
to the climatological loss frequency adopted from WACCM,
this model cannot account for interactions of circulation and
chemistry, which affects O3 mostly in winter polar regions
(see Sect. 5).
Atmos. Chem. Phys., 14, 7135–7147, 2014
3.2
CO results
Figure 8 shows that zonal mean cross sections of CO from
ACE-FTS, WACCM, and the trajectory model agree well
in the lower stratosphere. Here, instead of sampling trajectory results at the ACE measurement locations, we only
took zonal mean averages because, as mentioned in Park et
al. (2013), the differences between two processing are negligible. CO has a maximum in the tropical upper troposphere,
and decreases with altitude due to OH oxidation to a minimum near 22 km. CO increases above this altitude due to
production from methane (CH4 ) oxidation. The ACE-FTS
observations show high CO mixing ratios in the polar middle and upper stratosphere regions, resulting from the downward transport of CO from the mesosphere (from photodisassociation of CO2 ); this behavior is also seen (to a weaker
degree) in WACCM, but is not simulated in the trajectory
model, which does not include mesospheric processes.
Figure 9a shows the CO vertical profiles and time series averaged in the deep tropics (15◦ N–15◦ S). Due to the
differences of MLS and ACE-FTS retrievals in the stratosphere, here we also included ACE CO for comparison. The
vertical profiles in Fig. 9a show broad-scale agreements,
although there are differences among the trajectory models (with ERAi-driven results larger than those driven by
MERRA) and also between the ACE-FTS and MLS observations (all of the models agree better with the ACE observations above 22 km). Time series of CO at 100 hPa (Fig. 9b)
show a semi-annual cycle linked to initialized variations in
the upper troposphere (Liu et al., 2007), with approximate
agreement among the models and observations (with slightly
larger values in the MLS data). The variability changes to an
annual cycle at 68 hPa, as a response to variations in tropical
upwelling. At 68 hPa the annual cycle is captured in a reasonable manner in the models, with the ERAi results showing
better agreement with MLS and ACE-FTS data.
A further diagnostic to evaluate the model simulations is
made by plotting monthly tropical (15◦ N–15◦ S) averages of
O3 vs. CO in the lower stratosphere (68 hPa), as shown in
www.atmos-chem-phys.net/14/7135/2014/
2.4
2.4
2.4
7575
75
O3 (ppmv)
83
2.6
O (ppmv)
O33 (ppmv)
83
83
7575
2.6
2.6
75
2.1
2.1
2.1
6060
6060
60
60
T. Wang et al.: Trajectory
model simulations of O3 and
CO in the lower stratosphere
7141
Lon-Lat: lon[0,360],lat[30,60,60,90],,vert[150,10],time[2005.042,2011.958],
1.7
1.7
1.7
4545
4545
45
45
Lon-Lat: lon[0,360],lat[-90,-60,-60,-40],,vert[150,10],time[2005.042,2011.958],
X00: monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
X01: WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X00: grid_mon_mlsO3_noICE_noNeg_noSpike_lon360_monCat_2004-2011.nc; X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_3isob_mlsAK_monCat_2005-2011_100-1.nc;
X02: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
Fig. 7
MLS
3030
3030
30
WACCM
TRAJ_MERak
TRAJ_MERak
MLS
MLS
MLS
TRAJ_MERak
30
0.9
0.9
TRAJ_MER
0.9
TRAJ_MER
TRAJ_ERAi
TRAJ_ERAiak
Lon-Lat:
lon[0,360],lat[-90,-60,-60,-40],,vert[150,10],time[2005.042,2011.958],
Lon-Lat:
lon[0,360],lat[-90,-60,-60,-40],,vert[150,10],time[2005.042,2011.958],
Lon-Lat: lon[0,360],lat[-90,-60,-60,-40],,vert[150,10],time[2005.042,2011.958],
X00:X00:
grid_mon_mlsO3_noICE_noNeg_noSpike_lon360_monCat_2004-2011.nc;
X01:X01:
130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_3isob_mlsAK_monCat_2005-2011_100-1.nc;
grid_mon_mlsO3_noICE_noNeg_noSpike_lon360_monCat_2004-2011.nc;
130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_3isob_mlsAK_monCat_2005-2011_100-1.nc;
X00: grid_mon_mlsO3_noICE_noNeg_noSpike_lon360_monCat_2004-2011.nc;
X01: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_3isob_mlsAK_monCat_2005-2011_100-1.nc;
MLS
MLS
DJF
2.9
2.9
2.4
2.4
2.4
2.9
2.9
2.1
2.4
2.6
2.6
2.1
1.8
U
PV
1.81.8
2.4
2.4
24
-75
75
7575
-75-75
60
6060
-60-60
45
4545
a30-45
-45
30
30
-60
-45
b
U
PV
24
-75
1.8
2.4
2.4
1.5
(ppmv)
OO
O3 3(ppmv)
(ppmv)
68
O3 3(ppmv)
83
68
83
U
PV
24
O3 O
(ppmv)
3 (ppmv)
3 (ppmv)
OO
3 (ppmv)
68
8368 83
2.1
2.1
2.6
2.6
1.51.5
75
2.1
2.1
-60 60-75
1.21.2
1.7 -45 45-60
1.7
0.90.9
-45
30
30
0.9
0.9
0.60.6
7575
75
75
-75-75
-75
6060
60
60
-60
-60
45-60
45
45
45
-45
-45
-45
3030
30
30
1.5
1.2
2.1
2.1
1.2
0.9
1.7
1.7
c
SEP
0.9
0.6
0.9
0.9
0.6
2.4
2.3
2.42.4
2.4
2.1
2.0
2.1
2.12.1
1.8
1.8
1.8
1.5
1.5
1.6
1.2
d
-60
-60
45-60
1.51.5
75
60
-75 75
-75
60
-60 -60
45
0.90.9-45
f 30-45
-75-75
-75
1.21.2
-60
45-60-60
-45e -45
-45-45
30
-45
30-45
3
O3 (ppmv)
83
6868
-75
O3 (ppmv)
O3 (ppmv)
68
68
75
60
-75-75
O (ppmv)
1.81.8
1.2
1.2
0.9
0.9
0.6
0.6
0.6
0.60.6
Figure 7. Polar O3 distributions shown in MLS (left column), trajectory results driven by MERRA (middle column), and trajectory results
driven by ERAi (right column) during
Northern
Hemisphere
(DJF,
and Southern Hemisphere spring (September, SEP, d–f) at
2005-2011
DJF
and SEP,winter
average
ata–c)
68hPa
68 hPa. Trajectory results are weighted
by the MLS averaging
kernels. The 24 PVU potential vorticity level (a–c) and the 195 K temperature
DJF: MERx1.1
ERAix1.05
(d–f) are overlaid in black dashed lines for DJF 10
and SEP, respectively.
32
SEP: all original
68
100
4621
6818
10016
100
-60
-30
0
Lat. (O)
30
60
-9090
2126
26
22
1429
3124
18
4621
14
10
6818
-60 -60 -30
-30
-90
24
21
14
18
22
26
30
60
26
24
21
16
18
10016
0
30 60
0 O 30
Lat.
Lat. ( )( )
CO (ppbv)
CO
(ppbv)
60
90-60
90 -90
240
60
29
18
30
26
22
18
14
10
16
-30
O
10
32
0
Lat. (O)
30
60
CO (ppbv)
68
3124
30
26
Approx. Alt. (km)
46
2126
1032
Approx. Alt. (km)
46
-90
1429
31
31
c) TRAJ_MER
240
60
Pres. (hPa)
21
1032
CO (ppbv)
Pres. (hPa)
14
21
Approx. Alt. (km)
Pres. (hPa)
10
29
b) WACCM
Approx. Alt. (km)
14
a) ACE
Pres. (hPa)
Fig. 8
90
240
10Latitude-Vertical:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
14 18 22 26 30 60X01: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
240
X00: monthly_gridded_CO_noICE_screnNeg_monCat_2004-2011.nc;
X02: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
29
21
26
Fig. 10. This includes the observations from MLS,31 together
terns show a center of high CO over Central America and
with trajectory model simulations driven by both46MERRA
enhancements over southeastern Asia, extending to the tropand ERAi, which show the sensitivity to different Q68(see also
ical western Pacific (largely attributable to fossil fuel emis100 O3 and
Fig. 1). There is an overall anti-correlation between
sions; Jiang et al., 2007). The trajectory model also captures
results for reference
CO in Fig. 10, mainly representing the out-of-phase
well-knownERAi
CO maximum
linked to the Asian monsoon
2005-2011,
season
10 annual ALL the
14
cycles seen in Figs. 4b and 9b. The comparisons in Fig. 10
anticyclone during JJA, which is substantially stronger at the
21
show that stronger upwelling in the ERAi simulation
pro100 hPa level (e.g., Randel and Park, 2006; Park et al., 2009;
31
duces slightly lower values of O3 and higher values in CO
Randel et al., 2010).
46
(> 30 ppbv), in better agreement with MLS data. Moreover,
Overall, the large-scale seasonal behavior of CO simulated
68
the slope of the CO–O3 variations in the ERAi simulation
by the trajectory model is in agreement with both obser100
approximately matches the MLS result.
vations (MLS and ACE-FTS) and Eulerian chemical model
10
The DJF and JJA seasonal distributions of CO 14at 68 hPa
(WACCM), although the results are sensitive to the tropical
21
from the ERAi trajectory model are compared to MLS
data
upwelling speed (see Sect. 5).
31
in Fig. 11. In both seasons the trajectory model shows
spa46
tial patterns consistent with MLS data. During DJF
the pat-
24
21
18
16
29
Pres (hPa)
26
24
21
18
16
0
30
Lat. (O)
60
-60
-30
0
30
Lat. (O)
60
-60
-30
0
30
Lat. (O)
Atmos. Chem. Phys., 14, 7135–7147, 2014
24
60
30
26
22
18
14
10
24
60
30
26
22
18
14
10
32
29
26
Pres (hPa)
www.atmos-chem-phys.net/14/7135/2014/
-30
24
60
30
26
22
18
14
10
32
24
21
68
100
-60
Approx. Alt. (km)
32
14
Approx. Alt. (km)
10
Approx. Alt. (km)
Pres (hPa)
MLS
TRAJ_ERAi
TRAJ_MER
Figure 8. Zonal mean cross sections of CO from (a) ACE-FTS, (b) WACCM,
and (c) trajectory model
driven by MERRA reanalysis.
18
16
60
Vertical
Profile:lon[0,360],lat[-90,90],vert[150,1],time[2005.042,2011.958],
X02: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_3isob_mlsAK_mon
X00: 130721_s100_i370_mthd_MER_da
monthly_gridded_CO_noICE_scren
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
X02:
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
X02: 130721_s100_i370_mthd_MER_da
X00:
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i370_mthd_MER_day_Wa
X00:
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i370_mthd_MER_day_Wac
X00:
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i370_mthd_MER_day_W
Time Series:
lon[0,360],lat[
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03:
monthly_gridded
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_2isob_var1_monCat_2005-2011.nc;
X03:
mon
MLS
WACak TRAJ_MERak
TRAJ_ERAiak
ACE
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_2isob_var1_monCat_2005-2011.nc;
X03:
m
X00: WAC_nugByMER_a
MLS
WACak
o
MLS
WACa
Fig. 9
7142
2005-2011, 15 N-S average
WACCMTRAJ_MER
TRAJ_ERAi
MLS
WACCM
TRAJ_MER
WACCM
TRAJ_MER
ACE
TRAJ_ERAi
TRAJ_ERAi
X02: 130725_s100_i370_m
MLS
MLS
T. Wang et al.: Trajectory model simulations of O3 and CO in the lower stratosphere
WACC
CO(hPa)
(ppbv)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km) Pres
Pres (hPa)
Pres (hPa)
AltAlt
(km)
Approx.
(ppbv)Alt
CO(km)
Anom. (ppbv)
Approx.
(km)
Approx.
Alt
(km)
Pres (hPa) Approx.
Pres (hPa)CO Anom.
Approx.
Alt
(km)
Approx.
Alt
(km)
Approx.
Alt
(km)
Approx.
Alt
(km)
CO (ppbv)
CO (ppbv)
CO (ppbv)
CO (ppbv)
CO (ppbv)
CO (ppbv)
Pres (hPa)
CO (ppbv)
(ppbv)
CO(ppbv)
(ppbv)
CO
CO
Pres (hPa)
Pres (hPa)
10
10
10
WACCMTRAJ_MER
MLS 14.7ACE 31.6
TRAJ_MER
TRAJ_ERAi
MLS WACCM
TRAJ_MER
TRAJ_E
10.0
32 TRAJ_ERAi
10.0
32
N.Mid.Lat.[30,60]
N.Pole.[60,90]
14.7 31.6
90
31.6
31.690MLS
10030
hPa
29
100
hPa
100 hPa10.0
46.4
46.4
WACCM
TRAJ_MER
TRAJ_ERAi
MLS
WACCM
TRAJ_MER
TRAJ_ERAi
10.0
32
10.0
32
N.Mid.Lat.[30,60]
N.Pole.[60,90]
60
14.7
14.7
20
20
80
80
28
22 22
14.7 10.0
14.7 10.0 292222
32
21.5 46.4
21.5 N.Mid.Lat.[30,60]
29
70
70
N.Mid.Lat.[30,60]
[-18,0],[0,18]
N.M
68.1
68.1
[-15,0],[0,15]
46.4
46.4
46.4N.Pole.[60,90]
14.7
14.7
14.7
2040
25
21.5
21.5
29
29
WACCM
32
[-18,0],[0,18]
2014.7
22 22
10.0
Pres (hPa)
Pres (hPa)
Approx.
Alt (km)
Pres (hPa) Approx. Alt (km)
Approx. Alt (km)
29
32
[-18,0],[0,18]
CO (ppbv)
Pres (hPa)CO (ppbv)
14.7
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
10],time[2005.042,2011.958],
mygrid_3cord_200501-201112_aver_isob2.nc; X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_m
2006 2007
2008 2009 2010 2011
2006
2
2006
2008
[-15,0],[0,15]
N.Mid.Lat.[30,60]
N.P
,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
2006 2007
2007
2008
c_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03: monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
[-18,0],[0,18]
N.Mid.Lat.[30,60]
N.P
[-18,0],[0,18]
N.Mid.Lat.[30,60]
N
MER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
10.0
10.0
3232 68 hPa
370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_2isob_var1_monCat_2005-2011.nc;
X03: monthly_gridded_CO_noICE_screnNeg_monCat_20
323232 Vertical
3232
10.0
32
40
hPa
50 68
32
10.050
b)
X00: WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01: 130721_s100_i370_mthd_MER_day_Wac_P-L
a)10.010.0 50 68
68 hPa
hPa
Vertical Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
RAJ_MER
TRAJ_ERAi
MLS
X02: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03: monthly_gridded_mls
2006 20
X00: WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing
30
14.7 40
14.7 40
n[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
40
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03:
monthly_gridded_mlsO3_noICE_monCat_2004
ACCMTRAJ_MER
TRAJ_ERAi
MLS
ACE
14.7
14.7
28
28
28
yMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_m
14.7
14.7
2029
29 29
WACCM
TRAJ_MER
MLS
0_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03:TRAJ_ERAi
monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
5029
30 29 29Vertical
68 hPa
Vertical Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
30
Vertical Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
21.5
21.5 30
N.Mid.Lat.[30,60]
N.Pole.[60,90]
X00:
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i37
WACCM
TRAJ_MER
TRAJ_ERAi
MLS
10
X00: WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3fo
X00:
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_av
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
X02:WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_2isob_var1_monCat_
21.521.5
21.5
X02: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
21.520
10.0
32 TRAJ_MER
10.0WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
32 20
10.0X03: monthly_gridded_mlsO3_noICE_monCat_2
15,0],[0,15]
N.Mid.Lat.[30,60]
N.Pole.[60,90]
20 130721_s100_i370_mthd
X00:
X01:
WACCM
TRAJ_ERAi
MLS
X00:
X01:
24
24 X02: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_li
0130721_s100_i3
4024
31.6
31.6
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.n
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03: m
25 10.0
25
[-18,0],[0,18]
N.Mid.Lat.[30,60]
25
25
32
10.0
32
10.02525 N.Pole
Approx. Alt (km)
Pres (hPa)
46 100
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
O
Pres (hPa)
100 68
Pres (hPa)
Approx. Alt (km)
O
O
Pres (hPa)Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Pres Alt
(hPa)
Approx.
(km)
Pres
(hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
PresApprox.
(hPa) Alt (km)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
O
68
O
O
O
O
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Lat. (O)
Pres (hPa)
Approx. Alt (km)
PresApprox.
(hPa) Alt (km)
Pres (hPa)
Pres Approx.
(hPa) Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Approx.
Approx.Alt
Alt(km)
(km)
Approx.
Alt (km)
Approx.
Alt (km)
46
CO (ppbv)
Approx. Alt (km)
Pres (hPa)
Lat. (O)
Lon-Lat: lon[0,360],lat[-40,0,0,45],,vert[150,10],time[2005.042,2011.958],
X00: monthly_gridded_CO_noICE_screnNeg_monCat_2004-2011.nc; X01: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_3isob_mlsAK_monCat_2005-2011_100-1.nc;
Pres (hPa)
Pres (hPa)
Pres (hPa)
Approx.
Alt (km)
Pres (hPa)Approx. Alt (km)
Approx.
Alt
(km)
Lat. Lat.
() ()
Lat. Lat.
() ()
Approx.
Alt
(km)
Approx.
Alt
(km)
Lon-Lat: lon[0,360],lat[-40,0,0,45],,vert[150,10],time[2005.042,2011.958],
X00: monthly_gridded_CO_noICE_screnNeg_monCat_2004-2011.nc; X01: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_fineV_3isob_mlsAK_monCat_2005-2011_100-1.nc;
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
CO (ppbv)
Approx. Pres
Alt (km)
(hPa)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
PresAlt
(hPa)
Approx.
(km)
Pres
(hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
PresApprox.
(hPa) Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
PresApprox.
(hPa) Alt (km)
Approx. Alt (km) Pres (hPa)
Alt (km)
Approx.Approx.
Alt (km)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt
(km)
Pres
(hPa)
Approx. Alt (km)
Approx. Alt (km)
28
32
29
[-18,0],[0,18]
25
21.5
29 32
[-18,0],[0,18]
60
10.0
32 [-18,0],[0,18]
10.0 N.Mid.Lat.[30,60]
32N.Mid.Lat.[30,60]
32 10.0
32
60 14.717
1714.7
1732 10.0
14.7
21.5
21.5 10.0
25
2510.0 32 3231.6
29
29
2019
100 hPa
31.6
31.6
32
32
10.0 3
32
10.0
10.0
19 19
19
19
100.0
100.0
100
19
50
25
25
25
50
21.5
21.5
21.5
68.1
68.1
CO initial
68.1
68.1
θ=~370K
31.6
31.6
31.6
14.7
14.7
14.7
14.7
14.7
value
9013
MLS
TRAJ_ERAiak 21.5
25 2922
25 146.8
25 29
29 2231.6
29
29
40
21.5
28
28
0
31.6
40 21.513
13
146.8
14.7
14.7 14.7 2
46.4
22
29
25
252214.731.629 46.4
80
181616
16 46.4
16 16
100.0
31.629 46.429 100.0
31.6
16
100.0
100.0
22
22
3022
46.4
46.4
21.5
21.5
21.5
15
2006 0 2007
21.5
0 15 30 45 60 75 90
0 2007
1521.530
75 2011
90
152008
32
2006
2008 45
2009602010
2006
31.6
31.6
31.6
70
46.4
46.4
15
22 2519
22
22
25 19
2511.0
25
21.5
21.5
21.5
13 0.1
25
25
T
0.1 0.10.3 0.30.7
1.746.4
4.3
11.011.0
0.10 0.1 25
0.30.3 21.5
0.7
1.7
4.3
0.3
0.7
1.7
4.3
0.7
1.7
4.3
11.0
0.1
0.3
68.1
68.1
68.1
CO
(ppbv)
CO
(ppbv)
Time
46.42
60
19
22
2219 46.425
25
11
31.6 25
31.6 25
31.6 68.1
68.1
68.1
(ppmv)
19 O3 (ppmv)
19
31.6O3 O
31.6S.P
O3 (ppmv)
S.Mid.Lat.[-60,-30]
3 (ppmv)
46.4 Tropical:Lat.[-30,30]
46.4 -1519 31.6
46.4
9
68.1
68.1
50
31.6
31.6 31.6
Figure
9. Tropical
(a)AK
vertical profile and (b) time
series
panel) of MLS,
WACCM, ACE,
-30 68 hPa, upper
100.0
16results
100.0
16 bottom panel;
100.0
19 22
19(100
0 32
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Po
22 hPa,
22S.Mid.Lat.[-60,-30]
22
ure
10. model
32 using
10.0
32
10.0 and trajectory
Tropical:Lat.[-30,30]
S.
22
22In (b)19the22WACCM
68.1
68.1
68.1
◦ N–15◦ S from
16
100.0
16
100.0
modeled CO100.0
driven
by MERRA
wind
and
ERAi
wind,
averaged
over
15
2005
to
2011.
and
trajectory
Model
results
mainly
46.4 22 10.0
46.4
46.4
22
19
19
22
22
2
Model
results
mainly
32
32
10.0
32
46.4
46.4
16
100.0
16
100.0
16
32
10.0
32
10.0
32
Fig.In
11(a)
68.1
1.0
0.1results
0.3
0.7 1.7
4.3 averaging
0.1the 68.1
0.3 square
0.7 46.4
1.7 MLS
4.3initial
11.0
87
model
arecompare
weighted
by
MLS
kernels.
purple
indicates
the
CO values TRAJ_ERAiak
carried
by 68.1
parcels when
20
46.4
46.4100
46.4 2006
14.7
14.7
tothe
ACE,
so11.0
didn’t
30 compare
100.0
16
100.0
16
36
5216 19
68
85
4
20
36
52
68
85
4
20
36
52
68
85
to
MLS,
so
used
AK
78
O (ppmv)
O3 the
(ppmv)
160.1
they were 28
first injected
into the0.3
domain0.7
at 370
K, and4.3
vertical
bars in red indicate
resolutions
at each
of the MLS retrieval
2815
2819 0.3 0.7
1911.0
1911.0
19 MLS
0.1
1.7
0.3 vertical
0.7 68.1
1.7
19CO
19CO 4.3
30 0.1
68.1
68.1
use3AK
15
CO (ppbv) pressure levels.
(ppbv)
(ppbv) 14.7
100.0
16
100.0
1668.1
100.0 71
14.7
14.7
14.7
19
19
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
19
19
O3 (ppmv)
O3 (ppmv)
O3 (1
01.7 68.1
0.1 0.3
0.1
4.3 68.1
11.0
0.1 0.3
4.3 68.1
11.0
29 290.7 1.7 4.3 11.0
29 29
2929
15
21.5 0.3 0.7
21.5 0.7 1.7
68.1
68.1
63
14
cal:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
10.0
32
10.0
32
10.0
-15
0
O
(ppmv)
O
(ppmv)
O
(ppmv)
0.7 1.7Vertical
11.0 3
0.1 0.3 100.0
0.7 1.7 16 4.3 11.0 3
0.1 0.3 100.0
0.7 1.7 164.3 11.0
Tropical:Lat.[-30,30]
S.Pole.
16 4.3 Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
16 S.Mid.Lat.[-60,-30]
100.0
51
3
13 16
16
100.0
16
100.0
32100.0
2410.0 32 32
24-30
-15
013 24
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_L
21.5
O3 (ppmv)X00:Tropical:Lat.[-30,30]
10.0 10.0
32 100.0
10.0
32
16 21.5
16
21.5
21.5 10.0
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
31.6
31.6
16 O3 (ppmv)
16 O3 (ppmv)
100.0 100.0 1
ofile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03:
monthly_gridded_mlsO3_noICE_
-30
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11
0.1
0.3
0.7
1.7
4.3
11.0
0.1
a)
MLS
DJF
b)
TRAJ_ERAi
DJF
0
14.7
14.7
2525 85 0.310.0
25
32 29 25 25
10.0 14.7
32
10.0
pical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01:
130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_v
4
20
36
5225
68
85
4 32 20
36
52 4068
29 1.7
14.7
14.7
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
MLS
0.1
0.3
0.7
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
50 14.7
O
(ppmv)
O
(ppmv)
O
(ppmv)
O
(ppmv)
25_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03:
monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
3
3
14.7
14.7
46.4
46.4
32
10.02033029 3228
10.0 3 37
CO
(ppbv)
CO
(ppbv)
31.6
31.6
29 28
91 20
31.6
2010.0WACCM
O3 TRAJ_ERAi
(ppmv)
O3 S.Pole.[-60,-90]
(ppmv) 29
O31.6
TRAJ_MER
MLS 21.5 O3 (ppmv)
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
Tropical:Lat.[-30,30]
S.Mid
3 (ppmv)
15
30
14.7
14.7
14.7
21.5
21.5
31
69
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
29
29
29
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
0
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
15
3225 22
10.0
32
10.0
32
32
10.0
32
21.5
21.5
WACCM
TRAJ_MER
TRAJ_ERAi
MLS
22
22
40 21.5
22
22
22
25
26
14.7
14.7
14.7
63
68.1
68.1
21.5
21.5
32
10.0
32
10.0
29
29
-150
32
32
32
10.0 10.0 3
46.4
21
46.4
46.4
59
25 25
25 10.02510.021.532 31.6
25
21.5 31.6 46.4
21.5
17 [-18,0],[0,18]
17
17
31.6
-30
-15
N.Mid.Lat.[30,60]
N.Pole.[60,90]
0
14.7
14.7
14.7 31.649 29
31.6-30
25 29
25 100.0
25 29 31.6
100.0
30 31.6
29
29
21.5
21.5
21.5
14.7
14.7
31.6
120 14.7
180 240 300 28
36010.0
0
60 120 180 32
240 300 360 0 1919
10.0
14.7 14.7 2
28 N.Mid.Lat.[30,60]
2232 19 19
2232
[-18,0],[0,18]
19 190 6014.7
29d)N.Pole.[60,90]
25
29
Lon.
( 68.1
)
68.1
c) MLS25JJA
TRAJ_ERAi
JJA
68.1
31.629 46.429 68.1
31.6
22Lon. ( )31.6
46.4
46.4
22 22
22
22
13
146.8
13
146.8
13
10.0
32
10.0
32
10.0
21.5
21.5
21.5 46.433
46.4
46.4
20
31.6
31.6
31.6
21.5
21.5
46.4
46.4
30
14.7 22 25 29
14.722 25
22 25 29
25
21.5 21.5
21.5 21.5
30 25
29
100.0
16 16
100.0
1616
25 7525 90
256025 75 90
100.0
100.0
30
45 46.4
60
15
30 46.4
45
0 15 46.4
32
19 16 160 15
19
150
22
22
25
25
27 14.7
14.7
14.7
68.1
68.1
68.1
19
19 CO29
31.6
31.6 68.1
19
29 31.6
019
68.1
CO (ppbv)
(ppbv)
10 68.1
46.4 19
46.4
46.4
24
68.1
68.1
31.631.6
31.6
31.6
31.6
0.1Slope=-66.1
0.7 0.71.7Slope=-56.9
0.1
0.30.3
0.70.7 1.71.721.5
4.3
0.1
0.30.3
0.10.3 0.3Slope=-38.0
1.7 4.321.5
4.311.0
11.0
0.1 31.6
-15 0.1 22
19 22
1911.0
194.32211.0
22 1625
21 22
68.1
68.1
68.1
2=0.67
2=0.65100.0
100.0
1625
100.0
25
-30
R2=0.79 21.5RO
R
22
22
21.5
21.5
(ppmv)
O
(ppmv)
0
O
(ppmv)
O
(ppmv)
46.4
46.4
46.4
22
22
19
19
22
22
2
3
3
16
100.0
16
100.0
0 100.0
3
3
100.0
16
100.0
16
68.1 16
68.1
68.1
46.4
46.4
25 11.031.6
25 30011.0
46.4
46.4
46.4
46.4
00.7 60
120
180 4.3
240
36031.6
0
60 120 180 240 300 360
1.0
0.1
0.3
0.7
1.7
4.3
0.1
0.3
1.7
0.2
0.4
0.6
0.8
1.0
100.0
16
16 19 Lon. 68
)
36
5216 1968
85
4 (ppmv)
20 68.11.7
36 4.3
52 1911.0
68
85
4 ( )100.0
85
19 0.3Lon.
19 0.3 100
0.1 O
0.3
0.7
0.1(ppmv)
0.720
1.7 36 4.3 52
11.0 (22
0.131.6
0.7
68.1
68.1
O31.6
O31.6
22
22
3
100.0
16
100.0
16
100.0
3 (ppmv)
3
19
19
19
19
19
19
1
CO (ppbv) 0.1 0.3 0.7 1.7 4.3 11.0
CO
(ppbv)
CO
(ppbv)
O3 (ppmv)
O
(ppmv)
O
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
68.1
68.1
3of CO at 68
68.1 22
46.4
46.4
Figure 11.68.1
Comparison
hPa (∼ 20 km) during DJF (top68.1 68.1 3 (
22
O3 (ppmv)
O3row,
(ppmv)
O3MLS
(ppmv)
0.7 1.7 16
4.3 10.11.0
0.7row,1.7
4.3between
11.0
Figure
Monthly
variations0.1
of O3 0.3
vs. 100.0
CO0.7
in the 1.7
tropical
lower11.0
16 4.3
100.0
16d)
16b) 0.1
100.0
16
a and
and JJA0.3
(bottom
c and
(left)46.4
46.4
46.4
100.0
16
O3 (ppmv) stratosphere
(ppmv)
O
(ppmv)
16trajectory mod100.0
16
100.0 1
(15◦ N–15◦ S, 68 hPa) from MLS
1616O3and
100.0
16
19
19
19
3
and trajectory modeling driven by68.1
ERAi wind (right, weighted by100.0 100.0
68.1
0.1 driven
0.3by MERRA
0.7 1.7
11.0wind.
0.7 1.7 0.1 4.3 0.311.00.7 1.7
0.1 11.0
0.3 0.7 1.70.1 4.30.3 11
4.3
eling
wind 4.3
and ERAi
Trajectory0.1
results0.3 the
MLS
averaging
kernels).
Horizontal
wind
vectors
from
ERAi
19
19
20 0.7
40.3
68
0.1
0.336 1.7
0.752 4.3
1.7
4.385 11.0
0.1 200.7
0.3 36
0.7 52
1.7
4.3
11.0
11.0
1.7
4.3
11.0
O 68
(ppmv)
O3 (ppmv)
O0.1(ppmv)
68.1averaging kernels.0.14 0.3
68.1
68.185
are weighted by O
the3 (ppmv)
MLS
are3overlaid as reference. 3
CO
16
100.0 O3CO
16(ppbv)
100.0 O3 (ppmv)
16
O3 (ppmv)
O3(ppbv)
(ppmv)
(ppmv)
16 11.0
16 11.0
0.1 0.3 100.0
0.7 1.7 4.3
0.1 0.3 100.0
0.7 1.7 4.3
0.1 0.3 100.0
0.7 1.7
21.5
O3 (ppmv)
O3 (ppmv)
O (ppmv)
0.7 4 1.7
4.3 11.0
0.1 in 0.3
0.7 1.7 4.3 11.0
0.1 0.3 0.7 1.7 4.3 11.0
Interannual
variability
of tracers
the tropical
also show
that the annual cycles of O3 and CO above the 3
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90
O3 (ppmv)
O3 (ppmv)
O3 (ppmv)
lower stratosphere
tropopause (especially around 70 hPa)
are strongly influ32
10.0
32
10.0
32
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
enced by the tropical upwelling (Brewer–Dobson) circula10.0
32
10.0
32
10.0
The coherent seasonal variations in O3 and
CO
in
the
troption.
14.7
14.7
29
ical29
lower stratosphere demonstrate that transport processes
We further explore interannual variations29in the chemi14.7
14.7
14.7
29 concentrations in this recal tracers and links29
to changes in the upwelling circulahave a large impact on the chemical
21.5
21.5
gion.
calculations of Abalos et al. (2012,
tion. Figure 12 shows the interannual anomalies
25The Eulerian-mean
25 (by remov21.5 upwelling is the main driver of25
2013a) show that tropical
the
ing the21.5
annual cycle) in O3 and CO concentrations in the21.5
25
25
31.6 tropopause.
31.6
tropical lower stratosphere from
MLS observations and from
annual cycles in O3 and CO above the tropical
31.6
31.6calculations, and in addition anomalies
22 in diabatic31.6
Our22Lagrangian trajectory
model results (Figs. 4b and 22
9b)
trajectory
46.4
46.4
22
22
46.4
46.4
46.4
Atmos.
www.atmos-chem-phys.net/14/7135/2014/
19 Chem. Phys., 14, 7135–7147, 2014
19
19
68.1
68.1
19
19
68.1
68.1
68.1
16
100.0
16
100.0
16
100.0
16
100.0
16
100.0
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
0.3
TRAJ_ERAiak -0.2
Pres (hPa)
Pres (hPa)
driven by both MERRA and ERAi are able to simulate the
observed interannual anomalies in O3 and CO, in spite of
significant differences for the background seasonal cycle
(Fig. 4b and 9b).
Taking results from the MERRA run as an example,
the close relationship between anomalies in diabatic heating and O3 is quantified in Fig. 13a, which shows strong
anti-correlation with explained variance r 2 = 0.73 and slope
1O3 /1Q = −1.05 ± 0.14 (ppmv)/(K day−1 ). Similar strong
correlation is found for CO and Q anomalies (Fig. 13b),
with explained variance r 2 = 0.85 and slope of 31.11 ± 2.86
(ppbv)/(K day−1 ). The signs of the slopes in Fig. 13a–b are
opposite because of the different background vertical gradients for O3 and CO. These strong relationships between diabatic heating (Q) and tracer anomalies highlight the dominant role of tropical upwelling in controlling species with
strong vertical gradients near the tropical tropopause.
Figure 12 also highlights strong anti-correlations between
O3 and CO anomalies, which is further demonstrated in
Fig. 13c. Abalos et al. (2012) have shown that for the ide-
www.atmos-chem-phys.net/14/7135/2014/
species into the lower stratosphere; after that the chemical
production and loss control the net changes of concentrations along the trajectories. Because the MERRA negative
heating rates at 150–130 hPa (Fig. 1) prevent air ascending
to the stratosphere, we chose a relatively high initialization
level in the upper troposphere (370 K) in this study. However, we could have initiated parcels at lower altitude, such
as 355 K when using ERAi circulation, to extend the results
to the upper troposphere.
Trajectory modeled O3 and CO in the tropical lower stratosphere largely depend on the strength of upwelling (and to
a lesser degree on the amount of mixing with extratropics;
Abalos et al., 2013a). Stronger upwelling is linked to faster
transport, which results in less time for chemical production (for O3 ) or loss (for CO), leading to overall lower values of O3 (Fig. 4a) and higher values of CO (Fig. 9a). The
comparisons of MERRA and ERAi simulations that have
different tropical upwelling rates (Q), e.g., Fig. 1, clearly
demonstrate this sensitivity. The detailed differences in diabatic heating rates among reanalyses have been discussed
Atmos. Chem. Phys., 14, 7135–7147, 2014
Pres (hPa)
2
Pres (hPa)
Pres (hPa)
Pres (hPa)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Pres (hPa)
Q Anom.
(K/day)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
(K/day)
Pres (hPa) Q Anom.Pres
(hPa)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Q (K/day)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
hPa
100
hPa 21.5
29
19 68
100.0
0
0.1
0.1
Q (K/day)
Approx. Alt (km) Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
68.1 21.5
68.1 21.5
68.1
14.7
14.7
MER
ERAi 14.7
29
29
19
19
16 MER
16 ratio68.1
68.1
68.1idealized
25
25
the
of background
vertical
gradients for the
ERAi
21.5 100.0
21.5 100.0
21.5
16
100.0
16
100.0
16
100.0
25
21.5 0.7 1.7 2006
21.5 the
31.6 25
31.6 2008
31.6 is 25
2007
2009
2010
2011
2006
2007case2008 2009
situation
where
transport
dominant.
For
0.3 0.7 1.7
0.1 0.3
4.3 11.0
0.1 21.5
0.3
0.7 vertical
1.7 4.3
11.0
-5 4.3 11.0
0.0
0.0
16
16 2009
100.0
16
100.02010 2011
25
25
25
2006 100.0
2008
2010
2011
2006
2007
2008
2009
31.6
31.6
31.6
O3 (ppmv)
O1.7
(ppmv)
O
(ppmv)
0.1 22 0.3
0.72007
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
of22CO and O3 3in the tropics near 68 hPa,
MERRA trajec-10
0.63
0.2 the
-0.1
-0.1
O3 (ppmv) 46 hPa
OS.Pole.[-60,-90]
O (ppmv)
31.6
31.6
31.6
0.1 0.3 46.4
0.7 0.6
1.7
11.0
0.1 0.3 46.4
0.7 1.7
4.3 11.0
0.1 0.3 46.4
0.7 1.7
4.3 hPa
11.0
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
3 (ppmv)
22 464.3
22 tory
22 46
0.2a background
results
yield
gradient
ratio
of 3∼ −18.34
hPa
46
hPa
0.5
c)
Q
Anomaly
2011
2006
2007
2008
2009
2010
2011
O3 (ppmv) 32
O3 (ppmv) 32S.Mid.Lat.[-60,-30]
2
10.0
10.0
Tropical:Lat.[-30,30]
S.Pole.[-60,-90]
-0.2
46.4 10.0
46.4
-0.2
3 (ppmv)
22
22
22 −146.4 O
0.5
−1
Time
0.1
0.2
9
19
19
0.4
km
km 10.0
)S.Pole.[-60,-90]
for68.1
(dCO/dz)/(dO
32
10.0 68.1 32 (ppbv
32
10.0
S.Mid.Lat.[-60,-30]
3 /dz).
46.4 )/(ppmv
46.4 A linear
68Tropical:Lat.[-30,30]
hPa 68.1
2011
2006
2007 0.4
2008 2009 46.4
2010 2011
0.1
14.7 19 Time
14.7 19(Fig. 13c) gives
32
10.0 0.3
32
32 / O368.1
10.0 a ratio
0.1
68.1 14.7 19 fit29of the10.0
68.1
observed
CO
anomalies
9
29
19
19
19
14.7 100.0 29 16
14.7 100.0 0.0
14.7
6
100.0 0.329 16
0.0(ppbv
68.1 0.2
68.1± 3.40
68.1the ideal29 is close to
of −22.45
/ ppmv),
which
0.0
0.2
14.7
14.7
14.7
16
100.0
16
100.0
16
100.0
21.5
21.5
21.5 -0.1
Figure
Interannual
anomalies
of0.3
(a) O0.1
(b)
from
MLS
29 12.
29 CO
29 4.3 11.0
3 and1.7
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.7
4.3
11.0
0.1
0.3
0.7
1.7
ized result (slightly
5
25
0.1
-0.116 outside
21.5of the two-sigma uncertainty).
21.5
16and trajectory simulations
100.0by
16 and21.5
100.0
(red)-0.1
driven
MERRA
ERAi in 0.1 250.3 100.0
O3 (ppmv)
O1.7
0.7 0.0
4.3 11.0
0.7 O1.7(ppmv)
4.3 11.0
0.7 1.7 4.3 11.0
3 (ppmv)
250.1 0.3 21.5
25 This approximate
250.1 0.3
0.0
21.5 3
21.5 theory
agreement
with
highly
idealized
31.6
31.6
31.6 1.7
0.2
0.2
◦
◦
-0.2
O3 (ppmv) 100
O
(ppmv)
O
(ppmv)
the 25
tropical
(15 0.7
N–150.2
stratosphere
(68
hPa).
(c)
shows
0.1 0.3
1.7S) lower
4.3 11.0
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
4.3
11.0
Tropical:Lat.[-30,30]
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
0.2
hPa
100
hPa
3
3
25100 hPa
31.6 10.0
31.6
31.6
100
hPa 2010 2011 25
22
22S.Mid.Lat.[-60,-30]
further evidence
for O10.0
the
control
ofS.Pole.[-60,-90]
tropical lower
O32007
(ppmv)
O3 (ppmv) provides
2006 10.0
2009
2 2011
32
32
Tropical:Lat.[-30,30]
3 (ppmv)
interannual anomalies
of2008
total
diabatic
heating
0.1 rates from MERRA
0.1
31.6
31.6
31.6
46.4
46.4
46.4
0.1
0.1
Time
22
22
22
10.0 The 32 stratosphere O3 and CO by
10.0
10.0
S.Mid.Lat.[-60,-30]
S.Pole.[-60,-90]
variations32in upwelling.
and ERAi,Tropical:Lat.[-30,30]
which serves32in our model as the vertical velocity.
46.4 14.7
46.4 14.7
46.4
22
22
22
14.7
32
32
10.0
32
10.0
0.0averaging
0.0
9
29
29
19by the10.0
19
0.0
0.0
trajectory results68.1
are weighted
MLS
kernels.
46.4
46.4
46.4
68.1
68.1
14.7
14.7
14.7
29
29
29
19
19
68.1 21.5 19
68.1 21.5 -0.1
68.1
14.7 -0.1
14.7
14.7
21.5 -0.1
29
29
-0.129
19
19
19
5
Summary
and
discussion
6
100.0
16
100.0
16
100.0
68.1
68.1
68.1
5
25
25
21.5
21.5
21.5
16
100.0
16
100.0
16
100.0
-0.2
25
25
21.5 -0.2
21.5
31.6 -0.2
31.6 25
31.6 0.2
0.1 0.3 0.7 1.7 4.3 11.0
0.1 0.3
0.7 1.7 4.3 11.0
0.1 21.5
0.3 0.7-0.21.7 4.3 11.0
0.2
0.2
16 rates (upwelling)
100.00.2 68
16hPa
100.0
16
100.0
25
25
25
68
hPa
heating
from
reanalysis
at
68
hPa.
While
The
results
presented
here
demonstrate
that
the
domain31.6
31.6
31.6
O3 (ppmv)
0.168
0.3 0.7 O1.7
4.3 11.0
0.1 22 0.3 0.7 O1.7
4.368 11.0
0.1 0.3 0.7 1.7 4.3 11.0
hPa
3 (ppmv)
3 (ppmv)
2
22hPa
0.1
0.1
O3 time-mean
(ppmv)
O3 (ppmv)0.1
O3 (ppmv) the
31.6
31.6
31.6
0.3 46.4
0.7 0.1
1.7
0.1
0.3 heat0.7 1.7
4.3 11.0
0.1 0.3
0.7 is 1.7
4.3for11.0
46.4
46.4
there 0.1
are significant
differences
in
diabatic
forward
trajectory
model
useful
studying
22 4.3 11.0
22 filling,
22
O3 (ppmv)
(ppmv)
46.4 O3 (ppmv) transport of trace 22
46.4theO3lower
46.4
22
ing 22rates between
MERRA
ERAi
gases in
stratosphere. The O3
9
19 and 46.4
19
0.0(Fig. 1), interannual
0.0
46.4
46.4
68.1 0.0
68.1
68.1 0.0
changes in Q (Fig. 12c)
show good agreement. Figure
12
and
CO
simulations
are
complementary
to
modeling
H
O
19
19
19
2
68.1
68.1
68.1
-0.1
-0.1
-0.119
19 that interannual
19
6
100.0-0.1 anomalies
16
100.0
16
100.0
shows
in O
(mainly
controlled
by tropopause
temperature)
in68.1
that O3 and
68.1
68.1
3 and CO are strongly
16 oppositely signed
100.0
16 CO rely on both
100.0
16 chemical production
100.0
-0.2
to
initial
conditions
and
0.1 0.3 0.7 anti-correlated
1.7 4.3 11.0(due-0.2
0.1 0.3 -0.2
0.7 vertical
1.7 4.3 gradient)
11.0
0.1 0.3 0.7-0.2
1.7 4.3
11.0
16
100.0
16
100.0
16
100.0
O3 (ppmv)
O
(ppmv)
O
(ppmv)
0.1 0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
0.1
0.3
0.7
1.7
4.3
11.0
and closely linked to interannual
changes
in
diabatic
heatand
loss
rates,
and
are
sensitive
to
transport.
Initial
condi2006 20073 2008 2006
2009 2007
2010 2008
2011 2009 2010 3 2011
2006 2007 2008 2009
2006 2010
2007 2011
2008 2009
O3 (ppmv)
O3 (ppmv)
O3 (ppmv)
0.1 0.3 0.7 Fig.
1.7 12 4.3
11.0
0.1 calculations
0.3 0.7 1.7 Time
4.3 based
11.0
0.1 0.3provide
0.7 entry
1.7 4.3
11.0
Time
Time
Time
ing. Furthermore,
shows
that
trajectory
tions
on
observations
values
of
chemical
O3 (ppmv)
O3 (ppmv)
O3 (ppmv)
56
0.1
2
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
Approx. Alt (km)
Pres (hPa)
Pres (hPa)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Approx. Alt (km)
Pres (hPa)
2006 2007 2008 2009 2010 2011
-0.4
0.2
2006
2007 2008 2009 2010 2011
100
hPa
46
0.4
0.1
46
hPa
042,2011.958],
4
2011.nc;
0.2
X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
0.0
Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
2
al Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
T.
Wang
et al.: Trajectory model simulationsX01:
of130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_m
O3 and CO in the lower stratosphere
7143
42,2011.958],
0.0
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
anlyc_monCat_new_1979-2012.nc;
Vertical Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
TRAJ_ERAi -0.1
0
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03:
monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
-0.2
X00: WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc; X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetim
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
X02: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc; X03: monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc
-0.2
-2WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X00:
X01:
130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_m
2011
2006
2007 2008 2009
2010 2011
0.2
alized
case where
dominates tracer transports, the
0.2
WACCM
TRAJ_MER
TRAJ_ERAi
MLS
X02:
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03:upwelling
monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
hPahPa
100
-4 68
WACCM
TRAJ_MER
TRAJ_ERAi
MLS
6 46 hPa
10
0.1
ratio
of
tendencies
for
two
tracers
(χ1 , χ2 ) is closely related
0.1
2011
100 2006
hPa 2007 2008 2009 2010 2011
WACCM
TRAJ_MER
TRAJ_ERAi
MLS
54
to
the
ratio
of
the
respective
background
gradients:
0.2
0.0
0.0
[-18,0],[0,18]
N.Mid.Lat.[30,60]
N.Pole.[60,90]
2 46 hPa
5.042,2011.958],
2
10.0
32
10.0
32
10.0
[-18,0],[0,18]
N.Mid.Lat.[30,60]
N.Pole.[60,90]
0.1
Ai_anlyc_monCat_new_1979-2012.nc;
0
0
-0.1
-0.1
al Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
∂χ 1 ∂χ 2 10.0
32
10.0
32 ∂χ 1 ∂χ 2
32
10.0
[-18,0],[0,18]
N.Mid.Lat.[30,60]
N.Pole.[60,90]
WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_m
a)
O
Anomaly
-2
/
=
/
∼
constant.
(2)
3
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
0.0
-0.2
-5
-0.2
14.7X00: WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
14.7
14.7
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03: monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
32
10.0
32
10.0
10.0
∂t
∂t
∂z
∂z
X01:32130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetim
9
29
29
0.2
-4
Vertical
Profile:lon[0,360],lat[-90,90],vert[100,10],time[2005.042,2011.958],
68 2006
hPa 2007
14.7
14.7
14.7
X02: 130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03: monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc
2011
2008 2009 2010 2011
-10WAC_nugByMER_allChem_interp2mls_mygrid_3cord_200501-201112_aver_isob2.nc;
-0.1
X00:
29
29 X01: 130721_s100_i370_mthd_MER_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_m
29
10
0.1
Time2009
100
hPa
TRAJ_MER
TRAJ_ERAi
MLS
14.7
14.7
14.7
X02:10
130725_s100_i370_mthd_ERAi_day_Wac_P-L_allPacl_Long_O3forcing_lifetime_var1_monCat_2005-2011.nc;
X03:
monthly_gridded_mlsO3_noICE_monCat_2004-2011.nc;
2011 WACCM
2006
2007
2008
2010
2011
68
hPa
21.5
21.5
21.5
Integrating
this
equation
in
time
for
monthly
O
and
CO
3
295
29
29
-0.2
TRAJ_MER
TRAJ_ERAi
MLS
5
25 WACCM
25
5
0.2
21.5
21.5
21.5
0.0
0.2
100
hPa
anomalies
gives the relationship
46 hPa
WACCM
TRAJ_MER
TRAJ_ERAi
MLS
25
25
25
21.5
21.5
21.5
31.6
31.6
31.6
0
0.1
0
[-18,0],[0,18]
N.Mid.Lat.[30,60]
N.Pole.[60,90]
-0.1
0.1
25
25
25
31.6 10.0
31.6 10.0
31.6
22 [-18,0],[0,18]
22N.Mid.Lat.[30,60]
2
10.0
32
32
N.Pole.[60,90]
∂O3 ∂CO
0.0
-0.2
-5-5
Time
Series:
lon[0,360],lat[-15,0,0,15],,vert[150,10],time[2005.042,2011.958],
31.6
31.6
31.6
46.4 32
46.4 32
46.4
0.0
22
22
22
1O
/1CO
≈
/
;
(3)
10.0
10.0
32
10.0
[-18,0],[0,18]
N.Mid.Lat.[30,60]
N.Pole.[60,90]
3 X01: ERAi_anlyc_monCat_new_1979-2012.nc;
X00: MERRA_anlyc_monCat_new_1979-2012.nc;
Anomaly
-10 b) CO
2011
2006
2007
2008 lon[0,360],lat[-15,0,0,15],,vert[150,10],time[2005.042,2011.958],
2009 10.0
2010 201132
-10
Time Series:
∂z 32
46.4 14.7
46.4
-0.1
22
22
22 ∂z 46.4 14.7
14.7
32
10.0
10.0
10
-0.1
X00: MERRA_anlyc_monCat_new_1979-2012.nc;
X01: ERAi_anlyc_monCat_new_1979-2012.nc;
9
29
29
19
19
Time
68
hPa
46.4
46.4
46.4
68.1
68.1
14.7
14.7
14.7
2011
2006 68.1
2007 2008 2009 2010 2011
-0.25
29
29
19 Time
19 i.e., the ratio of monthly anomalies29
19
approximately follows
0.2
0.2
2011
Q Anom. (K/day)
O3 Anom. (ppmv)
(ppbv)
Approx.
Alt (km)
(km)
Q Anom.
(K/day) Approx.
O3 Anom.
(ppmv)
COAlt
Anom.
(ppbv) CO Anom.
2011
2
-0.10 Slope = -0.91±0.10
-0.10 -0.10
-0.15-0.05
-0.10 0.00
-0.05 0.05
0.00 0.10
0.05
-0.15 -0.15
-0.10 -0.10
-0.05
0.00
0.05
0.10
Q(K/day)
Anom. (K/day)
Q Anom.
Q Anom.
(K/day)
-6
-6 = -44.14±3.95
-6 Slope
0.10
-0.10 0.00
-0.05 0.05
0.00 0.10
0.05 0.15
0.10
-0.10 -0.10
-0.05 -0.05
0.00
0.05
0.10
0.15
O(ppmv)
O3 Anom.
O3 Anom.
(ppmv)
3 Anom. (ppmv)
0.15
T. Wang et al.: Trajectory model simulations of O3 and CO in the lower stratosphere
0.00 0.00
0.00
-0.05 -0.05
-0.05
R2 = 0.73
Slope
= -1.05±0.14
-0.10
-0.10 -0.10
-0.15 -0.15
-0.10 -0.10
-0.05
0.00
0.05
0.10
-0.15-0.05
-0.10 0.00
-0.05 0.05
0.00 0.10
0.05
Q Anom.
(K/day)
Q Anom.
Q(K/day)
Anom. (K/day)
4
4
2
2
2
2
2
0
-2
0
-2
0
-2
R2 = 0.85
Slope
= 31.11±2.86
-4
-4
-4
-0.15 -0.15
-0.10 -0.10
-0.05
0.00
0.05
0.10
0.10
-0.15-0.05
-0.10 0.00
-0.05 0.05
0.00 0.10
0.05
Q Anom.
(K/day)
Q Anom.
Q(K/day)
Anom. (K/day)
0
-2
0
-2
4
CO Anom. (ppbv)
0.05
4
CO Anom. (ppbv)
0.05 0.05
4
CO Anom. (ppbv)
0.10
4
CO Anom. (ppbv)
0.10 0.10
CO Anom. (ppbv)
0.15
O3 Anom. (ppmv)
0.15 0.15
O3 Anom. (ppmv)
O3 Anom. (ppmv)
Fig. 13 For MERRA run with Average Kernel
(a) !
(b) !
68 hPa
CO Anom. (ppbv)
7144
-6 Slope = 43.61±5.15
-6
-6
0.10
-0.15-0.05
-0.10 0.00
-0.05 0.05
0.00 0.10
0.05
-0.15 -0.15
-0.10 -0.10
-0.05
0.00
0.05
0.10
Q(K/day)
Anom. (K/day)
Q Anom.
Q Anom.
(K/day)
(c) !
Tracer
Background
2
Ratio = -18.34
0
-2
R2 = 0.68
Slope
-4
-4 = -22.45±3.40
-4
-0.10 -0.10
-0.05 -0.05
0.00
0.05
0.10
0.15
0.10
-0.10 0.00
-0.05 0.05
0.00 0.10
0.05 0.15
0.10
O3 Anom.
(ppmv)
O3 Anom.
O3(ppmv)
Anom. (ppmv)
0.15
Figure 13. Scatter plots of the anomalies of (a) O3 vs. Q, (b) CO vs. Q, and (c) CO vs. O3 at 68 hPa. The dots are monthly variations of
tracers from trajectory modeling driven by MERRA winds and diabatic heating rates; the black lines show the linear fit. The red line in (c) is
the theoretically estimated tracer ratio estimated from the respective background gradients, using simplified relation in Eq. (3).
by Wright and Fueglistaler (2013), who highlight differences
in the corresponding long-wave radiative heating rates in the
lower stratosphere, which are influenced by both temperature
and ozone. We also conducted a sensitivity study of increasing MERRA diabatic heating rates (Q) by constant factors,
and the best overall fit to the observations is 1.5 times the
MERRA Q values, consistent with the ERAi-based results.
Although better agreement with observations of CO in the
tropical lower stratosphere is found using ERAi data, there
is reason to suspect that the ERAi diabatic heating in this
region may be too high. For example, Ploeger et al. (2012)
performed a radiative calculation showing that ERAi heating rates are ∼ 40 % too high, consistent with Schoeberl et
al. (2012), who show that trajectory modeled water vapor
tape recorder signal based on ERAi heating rates is ∼ 30 %
too fast compared with MLS observations. In spite of the
detailed differences, the trajectory modeled O3 shows reasonable simulation of the large-scale seasonal structure compared to both MLS and WACCM, including both the tropics
and the polar regions. The trajectory modeled CO in the tropical stratosphere is more sensitive to the MERRA vs. ERAi
differences, likely because of the shorter photochemical lifetime of CO in the lower stratosphere compared to O3 .
The annual cycles in O3 and CO in the tropical lower
stratosphere are reproduced in the trajectory model simulations, and the magnitude of variations provides a useful
test of the imposed circulation. The variability of O3 and
CO shows significant correlations with fluctuations in diabatic heating, for both seasonal and interannual timescales.
These close relationships support the concept that tropical upwelling plays a key role in regulating variability for
chemical species with strong vertical gradients in the lower
stratosphere (and explains the observed compact relationships among interannual anomalies in diabatic heating, O3 ,
and CO seen in Figs. 12–13). For the idealized situation
where upwelling dominates tracer transports, the tracer ratios can be expressed as ratios of the background vertical
gradients, and the observed O3 and CO changes are in approximate agreement with this expectation (Fig. 13c).
Atmos. Chem. Phys., 14, 7135–7147, 2014
The discussion above linking seasonal or interannual
changes in O3 and CO with chemical changes along slower
or faster upward trajectories is a Lagrangian perspective on
transport (appropriate for our Lagrangian trajectory model).
In contrast, the discussion linking O3 and CO variations at
particular pressure levels to varying circulations acting on
background vertical gradients (Sect. 4) is an Eulerian perspective. These two perspectives are complementary and do
not contradict each other; Abalos et al. (2013b) have recently
shown the equivalence of Lagrangian and Eulerian transport
calculations in the tropical lower stratosphere, highlighting
that each can provide useful information.
One limitation of this model is that it cannot account
for interactions of circulation and chemistry, due to the climatological chemical production rates and loss frequencies
adopted from WACCM (Sect. 2.2). This is most likely important in winter polar regions, where O3 loss chemistry due
to chlorine activation on polar stratospheric clouds is highly
temperature dependent. Although we cannot simulate the detailed behavior in any particular (cold) year with climatological chemical ozone loss rates, our model does a reasonable
job at simulating the time-average behavior of polar regions.
Our simulations with O3 and CO have demonstrated the
viability of the domain-filling forward trajectory model for
simulating species with relatively simple chemistry, and extension to other species would be straightforward. There are
several potential applications for such a trajectory model,
including describing parcel histories that characterize different transport pathways, and evaluating the importance of
tropical–extratropical exchanges. For example, trajectories
can allow tracing the sources of CO-rich air in the summertime Asian monsoon region, and quantifying the fate of the
parcels after breakup of the anticyclone. The model can also
allow detailed comparisons of transport based on different
and new reanalysis data sets, or idealized studies of the chemical responses to UTLS circulation in a changing climate.
www.atmos-chem-phys.net/14/7135/2014/
T. Wang et al.: Trajectory model simulations of O3 and CO in the lower stratosphere
Acknowledgements. We thank Kenneth Bowman, Laura Pan,
Mijeong Park, Marta Abalos, Dominik Brunner, and Cameron
Homeyer for their helpful discussions and comments. This
work was supported by NSF AGS-1261948, NASA grant
NNX13AK25G, and partially under the NASA Aura Science
Program. This work has been partially carried out during visits of
Tao Wang funded by the Graduate Student Visitor Program under
the Advanced Study Program (ASP) at the National Center for Atmospheric Research (NCAR), which is operated by the University
Corporation for Atmospheric Research, under sponsorship of the
National Science Foundation.
Edited by: P. Jöckel
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