JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 108, NO. D22, 4698, doi:10.1029/2002JD002824, 2003

Analysis of ozone loss in the Arctic stratosphere during the late
winter and spring of 1997 using the Chemical Species
Mapping on Trajectories (CSMT) technique
Akiko Kagawa1 and Sachiko Hayashida
Faculty of Science, Nara Women’s University, Nara, Japan
Received 5 August 2002; revised 25 May 2003; accepted 14 July 2003; published 21 November 2003.

[1] A scheme to create synoptic maps of stratospheric minor species from asynoptic

satellite measurements using a photochemical box model and trajectory analysis wasdeveloped and named Chemical Species Mapping on Trajectories (CSMT). CSMT
combined with Improved Limb Atmospheric Spectrometer (ILAS) data were used to study
the Arctic ozone loss mechanism in the late winter and early spring of 1997. Longand short-lived species in the stratosphere were successfully mapped by the CSMT
initialized with ILAS-observed ozone, nitric acid, and nitrous oxide. Comparisons of
CSMT-derived data and ozonesonde data, and/or other satellite data, validated the scheme.
The comparisons showed the reliability of the scheme in mapping long- and short-lived
species. A chemical ozone loss amount was estimated using the chemical model; themaximum ozone loss rate was about 34 ppbv/day in late February. The integrated ozone
loss from 13 January to 31 March was 41%, averaged over the entire polar vortex.
The effects of differences in polar stratospheric cloud composition and a possible warm
bias in the temperature data set were examined, and only minor differences were found in
the ozone loss amount. The derived ozone loss rates and integrated ozone loss are
consistent with results from other studies. The Arctic ozone loss in the late winter andearly spring of 1997 depended significantly on latitude and showed complex features:ozone loss occurred mainly at lower latitudes until late February; the region of significant
ozone loss shifted to higher latitudes in March. The CSMT scheme shows good potential
for various applications including detailed analyses of chemical mechanisms in the
INDEX TERMS: 0340 Atmospheric Composition and Structure: Middle atmosphere—
atmosphere.
composition and chemistry; 0341 Atmospheric Composition and Structure: Middle atmosphere—constituent
transport and chemistry (3334); 3337 Meteorology and Atmospheric Dynamics: Numerical modeling and data
assimilation; 3349 Meteorology and Atmospheric Dynamics: Polar meteorology; 9315 Information Related toGeographic Region: Arctic region; KEYWORDS: Arctic ozone loss, model, data assimilation
Citation: Kagawa, A., and S. Hayashida, Analysis of ozone loss in the Arctic stratosphere during the late winter and spring of 1997
using the Chemical Species Mapping on Trajectories (CSMT) technique, J. Geophys. Res., 108(D22), 4698,
doi:10.1029/2002JD002824, 2003.

1. Introduction[2] In the Arctic stratosphere, substantial ozone loss has
been observed in late winter and early spring over the past
decade [World Meteorological Organization (WMO), 1999].
Loss mechanisms are similar to those of the Antarctic ozone
loss. Because Arctic dynamics are more complicated,
however, the ozone loss evolves in a different manner. For
example because the Arctic vortex is smaller and more
distorted than the Antarctic vortex, Arctic air can be exposed
to sunlight, even during the polar night. Furthermore, there issignificant interannual variability in the spatial distributionof Arctic ozone. Because of the more complex dynamics in
1

Now at Fujitsu FIP Corporation, Tokyo, Japan.

Copyright 2003 by the American Geophysical Union.
0148-0227/03/2002JD002824$09.00

ACH

the Arctic vortex, separation of chemical ozone loss is more
important over the Arctic, and several sophisticated methods
have recently been developed to differentiate dynamical
effects from observed ozone changes. One method considers
the relationship between ozone and inert tracers, such as
N2O, to quantify the chemical ozone loss [e.g., Manney etal., 1994]. Another (the ‘‘Match’’ technique [e.g., Rex et al.,1999]) derives chemical ozone loss from a pair of balloon
soundings that measure ozone in the same air parcel at
different points on the same trajectory. Observed chemicalozone loss has been confirmed by these methods.
[3] Many models have been developed to investigate polarozone loss [e.g., Brasseur et al., 1997; Portmann et al., 1996].
Chemical-transport models (CTM) are powerful tools forstudying polar ozone loss, and have advanced the scientificunderstanding of Arctic ozone loss [Chipperfield andPyle, 1998; Guirlet et al., 2000; Hansen and Chipperfield,

3-1

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3-2

HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

1999; Lefevre et al., 1998]. CTMs are computationallyexpensive, however. For local comparisons in a limited area,
a box model that is integrated along trajectories is preferred.
Becker et al. [1998, 2000] compared the ozone loss derived
from a Match analysis to that estimated from a chemical box
model along the same trajectory in the Arctic winter andspring of 1992 and 1995. Model calculations for January
underestimated ozone loss, while those for February andMarch agreed with Match analysis. The inconsistency ofozone loss suggested an important unresolved issue in polarozone chemistry [Becker et al., 1998, 2000].
[4] A significant ozone reduction occurred over the Arctic
in the late winter and early spring of 1997, a year that wasmarked by the presence of a polar vortex into early spring[Newman et al., 1997]. In 1996/1997, temperatures did not
fall to PSC formation temperatures (195 K) until early
January, which is relatively late as compared to other years.
However, temperatures remained low through the end of
March and a strong and symmetric vortex persisted into early
May [Coy et al., 1997]. Significant Arctic ozone loss wasnoted in 1997 by observations and models [Chipperfield andPyle, 1998; Guirlet et al., 2000; Knudsen et al., 1998; Hansen
and Chipperfield, 1999; Newman et al., 1997; Lefevre et al.,
1998; Manney et al., 1997; Müller et al., 1997; Sinnhuber etal., 1998; Schulz et al., 2000; Terao et al., 2002].
[5 ] The Improved Limb Atmospheric Spectrometer(ILAS) instrument developed by the Environmental Agencyof Japan (EA) on board the Advanced Earth Observing
Satellite (ADEOS) was launched in August 1996, andsuccessfully observed ozone and ozone-related species suchas HNO3, NO2, N2O, H2O, CH4 and aerosols during thissame winter [Sasano et al., 1999]. ILAS was a solar
occultation sensor with an infrared spectrometer, and provided vertical profiles of chemical species with 1-km heightintervals. ILAS measured more than 6000 occultation
events in the Northern and Southern Hemisphere from
November 1996 to June 1997. ILAS observed 14 circumpolar points in each hemisphere daily at high latitudes(57.1 – 72.2N and 64.3 – 88.2S). An advantage of the
ILAS measurements is that many minor chemical species
can be observed simultaneously, allowing a detailed investigation of ozone loss mechanisms. Analysis of ILAS ozone
data by Terao et al. [2002] showed a maximum chemicalozone loss of 50– 80 ppbv/day in late February between
q = 400 K and q = 600 K. This ozone loss correlates wellwith the appearance of ILAS-derived PSCs, as reported by
Sasano et al. [2000] and Hayashida et al. [2000a], suggesting chlorine activation by heterogeneous reactions on the
PSCs. Analysis of ILAS HNO3 revealed large-scale denitrification after late February [Kondo et al., 2000; Irie et al.,2001], and Pan et al. [2002] reported a significant dehydration, which may have altered the chemical conditions
under which chemical ozone loss occurred.
[6] In this study, synoptic maps of ozone loss wereconstructed based on results from a chemical box modeland trajectory analysis, using ILAS measurements of minor
species as initial conditions. Chemical mechanisms of ozone
destruction in the late winter and early spring of 1997 were
investigated based on the synoptic maps.
[7] Several schemes have been developed to address thedifficulties of creating synoptic maps from asynopticallygathered trace gas data. These methods include the Kalmanfilter [Haggard et al., 1986], the Salby-Fourier technique[Salby, 1982a, 1982b], and constituent reconstruction[Schoeberl et al., 1989]. Pierce et al. [1994] used meteorological information to create synoptic maps. Morris et al.
[1995, 2000] developed ‘‘Trajectory Mapping,’’ which
creates synoptic maps from asynoptic satellite data byadvecting fields backward or forward in time on an analyzed field. Trajectory mapping has successfully madesynoptic maps of long-lived minor constituents.
[8] Here we incorporated a chemical box model into
trajectory mapping to estimate chemical change in theconcentration of real-time chemical species. The scheme
was named ‘‘Chemical Species Mapping on Trajectories(CSMT).’’ Chemical species from satellite measurement
points are time-integrated toward a target time along trajectories that start at different times, and a synoptic map ofchemical species is obtained. The CSMT is a useful method
to understand the behavior of minor species, especiallywhere complicated chemical/dynamical processes evolve,
as in the Arctic because spatial distributions of long- andshort-lived chemical species can be described on a global
scale. In addition, the CSMT scheme can expand the area ofgeographically limited data coverage, which enhances thepotential use of the data. In this study, chemical species
were successfully mapped from January to March using O3,
HNO3, and N2O data observed with ILAS. HNO3 isespecially important in determining the exact amounts ofozone loss because ozone loss is affected by HNO3 concentration through ClOx deactivation and PSC formation.
However, the microphysics of PSCs as well as HNO3related chemistry are not well understood and are still underinvestigation [Gao et al., 1999; Salcedo et al., 2001],
making the modeling of HNO3 concentration difficult. We
used colocated measurements of HNO3 to initialize themodel, and obtained a more realistic estimate of ozone loss.
Details of the chemical box model are given in section 2,
and the CSMT technique is explained and validated in
section 3. Results are discussed in section 4 and concluding
remarks are in section 5.

2. Photochemical Box Model[9] In a numerical simulation of atmospheric chemicaltransport, an integration of a set of ordinary differential
equations describing chemical transformations is needed.
Several techniques to integrate such systems efficiently
have been developed. For example, RODAS (RungeKutta-Rosenbrock Solver) [Sandu et al., 1997a, 1997b] iswell suited for application to atmospheric simulations andoffers good accuracy and efficiency and is used in this study.
The solver was provided by the Atmospheric Chemistry
Division of the National Center of Atmospheric Research(ACD/NCAR). RODAS uses a Runge-Kutta method to
calculate implicitly the temporal evolution of chemical minor
species. We incorporated 59 chemical species (Appendix A),
101 gas-phase reactions (Table 1), 48 photodissociations(Table 2), and 7 heterogeneous reactions (Table 3) into themodel. The code to calculate the photodissociation coefficients (J ) is based on the Garcia-Solomon two-dimensionalmodel [Solomon et al., 1985]. Kinetic rates for gas-phasereactions and absorption cross-sections for photodissociation
coefficients are from DeMore et al. [1997] and Sander et al.

HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

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3-3

Table 1. Gas-Phase Chemical Reactions Included in the Model With the Corresponding Reaction Rates and Referencesa
Reaction

Rate Coefficient

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
(25)
(26)
(27)

O + O + M ! O2 + M
O + O2 + M ! O3 + M
O + O3 ! 2O2
O(1D) + M ! O + M
CH4 + OH ! CH3 + H2O
CH3 + O2 + M ! CH3O2 + M
CH3O2 + NO ! CH3O + NO2
CH3O2 + CH3O2 ! 2CH3O + O2
CH3O2 + CH3O2 ! CH2O + CH3OH + O2
CH3O + O2 ! CH2O + HO2
CH3O + NO2 + M ! CH3ONO2 + M
CH3O2 + HO2 ! CH3OOH + O2
CH3OOH + OH ! CH3O2 + H2O
CH2O + OH ! CHO + H2O
CH2O + O ! CHO + OH
CH2O + Cl ! HCl + CHO
CH2O + NO3 ! CHO + HNO3
CHO + O2 ! CO + HO2
CO + OH ! HO2 + CO2
CH4 + O(1D) ! CH3 + OH
CH4 + O(1D) ! CH2O + H2
CH4 + O(1D) ! CH3O + H
H2O + O(1D) ! 2OH
H2 + O(1D) ! H + OH
H + O2 + M ! HO2 + M
OH + HO2 ! H2O + O2
OH + HNO3 ! H2O + NO3

(28)
(29)
(30)
(31)
(32)
(33)
(34)
(35)
(36)
(37)
(38)
(39)
(40)
(41)
(42)
(43)
(44)
(45)
(46)
(47)
(48)
(49)
(50)
(51)
(52)
(53)
(54)
(55)
(56)
(57)
(58)
(59)
(60)
(61)
(62)
(63)
(64)
(65)
(66)
(67)
(68)
(69)
(70)

OH + NO2 + M ! HNO3 + M
HO2 + NO2 + M ! HO2NO2 + M
HO2NO2 + M ! HO2 + NO2 + M
HCl + OH ! H2O + Cl
ClO + HO2 ! HOCl + O2
H + O3 ! OH + O2
OH + O ! O2 + H
HO2 + O ! O2 + OH
OH + O3 ! HO2 + O2
HO2 + O3 ! OH + 2O2
HO2 + NO ! OH + NO2
HO2 + HO2 ! H2O2 + O2
H2O2 + OH ! HO2 + H2O
OH + N ! NO + H
OH + H2 ! H2O + H
N2O + O(1D) ! 2  NO
N2O + O(1D) ! N2 + O2
N + O2 ! NO + O
N + NO ! N2 + O
NO + O3 ! NO2 + O2
NO2 + O ! NO + O2
NO2 + O3 ! NO3 + O2
NO2 + NO3 + M ! N2O5 + M
N2O5 + M ! NO2 + NO3 + M
CH3Cl + OH ! H2O + Cl
CH3CCl3 + OH ! H2O + 3  Cl
CCl4 + O(1D) ! ClO
CCl4 + O(1D) ! O + CCl4
CFC11 + O(1D) ! ClO
CFC11 + O(1D) ! O + CFC11
CFC12 + O(1D) ! ClO
CFC12 + O(1D) ! O + CFC12
CFC113 + O(1D) ! ClO
HCFC22 + O(1D) ! ClO
HCFC22 + O(1D) ! O + HCFC22
HCFC22 + OH ! H2O
HCFC141 + O(1D) ! ClO
HCFC141 + O(1D) ! O + HCFC141
HCFC142B + OH ! H2O
HCFC142B + O(1D) ! ClO
HCFC142B + O(1D) ! HCFC142B + O
CH4 + Cl ! HCl + CH3
Cl + O3 ! ClO + O2

4.23e-28  (1./T)  (1./T)  A(6.e-34,2.4,1.,0.)  0.2095
8.e-12  exp(2060./T)
0.2095  3.2e-11  exp(70./T) + 0.7905  1.8e-11  exp(110./T)
2.45e-12  exp(1775./T)(4.5e-31,3.,1.8e-12,1.7)  0.2095
3.0e-12  exp(280./T)
2.5e-13  exp(190./T)  0.3
2.5e-13  exp(190./T)  0.6
3.9e-14  exp(900/T)  0.2095(1.1e-28,4.,1.6e-11,1.)
3.8e-13  exp(800./T)
3.8e-12  exp(200./T)  0.7
1.0e-11
3.4e-11  exp(1600./T)
8.1e-11  exp(30./T)
5.8e-16
3.5e-12  exp(140/T)  0.2095
1.5e-13  (1. + 0.6  (press./1013.))
1.5e-10  0.75
1.5e-10  0.05
1.5e-10  0.2
2.2e-101.1e-10(5.7e-32,1.6,7.5e-11,0.)  0.2095
4.8e-11  exp(250./T)(X1 + X3/(1. + X3/X2))
X1 = 2.4e-14  exp(460./T)
X2 = 2.7e-17  exp(2199./T)
X3 = 6.5e-34  exp(1335./T)  A(2.4e-30,3.1,1.7e-11,2.1)(1.8e-31,3.2,4.7e-12,1.4)(29)/(2.1e-27  exp(10900./T)  A)
2.6e-12  exp(350./T)
4.8e-13  exp(700./T)
1.4e-10  exp(470./T)
2.2e-11  exp(120./T)
3.e-11  exp(200./T)
1.5e-12  exp(880./T)
2.e-14  exp(680./T)
3.5e-12  exp(250./T)
2.3e-13  exp(600./T) + 1.7e-33  exp(1000./T)  A
2.9e-12  exp(160./T)
5.e-115.5e-12  exp(2000./T)
6.7e-11
4.9e-111.5e-11  exp(3600./T)
2.1e-11  exp(100./T)
3.0e-12  exp(1500./T)
5.6e-12  exp(180./T)
1.2e-13  exp(2450./T)(2.e-30,4.4,1.4e-12,0.7)(50)/(3.e-27  exp(10991/T)  A)
4.0e-12  exp(1400./T)
1.8e-12  exp(1550./T)
3.3e-10  0.9
3.3e-10  0.1
2.3e-10  0.9
2.3e-10  0.1
1.4e-10  0.9
1.4e-10  0.1
2.e-10  0.9
1.0e-10  0.9
1.0e-10  0.1
1.2e-12  exp(1650./T)
2.6e-10  0.9
2.6e-10  0.1
1.3e-12  exp(1800./T)
2.2e-10  0.9
2.2e-10  0.1
9.6e-12  exp(1360./T)
2.3e-11  exp(200./T)

Reference
JPL94
JPL00
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL00
JPL97
JPL97
JPL00
JPL00

JPL00
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL00
JPL00
JPL00
JPL97
JPL97
JPL97
JPL97
JPL97
JPL00
JPL00
JPL97
JPL97
JPL00
JPL00
JPL97
JPL00
JPL00
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL00
JPL00

ACH

3-4

HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

Table 1. (continued)
Reaction
(71)
(72)
(73)
(74)
(75)
(76)
(77)
(78)
(79)
(80)
(81)
(82)
(83)
(84)
(85)
(86)
(87)
(88)
(89)
(90)
(91)
(92)
(93)
(94)
(95)
(96)
(97)
(98)
(99)
(100)
(101)

ClO + O ! Cl + O2
ClO + NO ! Cl + NO2
Cl + H2 ! HCl + H
Cl + HO2 ! HCl + O2
Cl + H2O2 ! HCl + HO2
HCl + O ! OH + Cl
ClO + NO2 + M ! ClONO2 + M
HOCl + OH ! H2O + ClO
ClO + ClO + M ! Cl2O2 + M
ClOO + M ! Cl + O2 + M
Cl2O2 + M ! 2  ClO + M
ClO + OH ! HO2 + Cl
ClO + OH ! HCl + O2
ClO + ClO ! OClO + Cl
ClO + ClO ! ClOO + Cl
ClO + ClO ! Cl2 + O2
Br + O3 ! BrO + O2
BrO + O ! Br + O2
BrO + NO ! Br + NO2
BrO + ClO ! Br + OClO
BrO + ClO ! Br + ClOO
BrO + ClO ! BrCl + O2
BrO + BrO ! 2  Br + O2
Br + HO2 ! HBr + O2
Br + H2O2 ! HBr + HO2
HBr + OH ! Br + H2O
HBr + O ! Br + OH
BrO + NO2 + M ! BrONO2 + M
BrO + HO2 ! HOBr + O2
Br + CH2O ! HBr + CHO
CH3Br + OH ! Br

Rate Coefficient
3.0e-11  exp(70./T)
6.4e-12  exp(290./T)
3.7e-11  exp(2300./T)
1.8e-11  exp(170./T)
1.1e-11  exp(980./T)
1.0e-11  exp(3300./T)(1.8e-31,3.4,1.5e-11,1.9)
3.0e-12  exp(500./T)(1.59e-32,4.5,1.36e-12,3.09)(2.7e-33,1.5,1,0)/(5.7e-25  exp(2500./T)  A)
(79)/(1.27e-27  exp(8744./T)  A)
7.4e-12  exp(270./T)
3.2e-13  exp(320./T)
3.5e-13  exp(1370./T)
3.0e-11  exp(2450./T)
1.0e-12  exp(1590./T)
1.7e-11  exp(800./T)
1.9e-11  exp(230./T)
8.8e-12  exp(260./T)
9.5e-13  exp(550./T)
2.3e-12  exp(260./T)
4.1e-13  exp(290./T)
2.4e-12  exp(40./T)
1.5e-11  exp(600./T)
1.e-11  exp(3000./T)
1.1e-115.8e-12  exp(1500./T)(5.2e-31,3.2,6.9e-12,2.9)
3.4e-12  exp(540./T)
1.7e-11  exp(800./T)
4.e-12  exp(1470./T)

Reference
JPL00
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
Bloss et al. [2001]
JPL97
JPL00
JPL00
JPL00
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL00
JPL00
JPL00
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97
JPL97

a
A, air number density; press, pressure (hPa). JPL94 represents DeMore et al. [1994], JPL97 represents DeMore et al. [1997], and JPL00 represents
Sander et al. [2000]. Reactions of CFC and HCFC with O(1D) were assumed to produce ClO with a quantum yield of 0.9, and the other branch wast b) ; k1(t) = c assumed to be quenched [Warren et al., 1991]. Three body reaction rates are expressed for (a, b, c, and d) as: k = x1  0:6x2 ; k0(t) = a  (300ðtÞA
k0 ðtÞA 2 1t d
) ; x1 = 1þðk0kð0tÞA=k
;
and
x
=
(1
+
(log
(
))
)
.
(300
2
10 k1 ðt Þ
1 ðt ÞÞ

[2000] except a reaction rate of ClO + ClO + M (reaction 79
in Table 1) which was recently updated by Bloss et al. [2001](Appendix A; Table 1).
[10] Polar chemistry is very sensitive to sunlight. Thus
ultraviolet radiation for large solar zenith angles (SZA) was
carefully examined before the model was applied to polar
regions in winter and spring. The air mass factor when the
SZA exceeded 90 was determined using the Chapman
function (see Appendix B).
[11] PSC growth governed by the temperature change
along trajectories is included in the calculations. Supercooled ternary solutions (STS) are assumed because STS
are the most likely event in the Arctic stratosphere [e.g., Dyeet al., 1992]. Nitric acid trihydrate (NAT) is assumed to bepresent because NAT is also observed [e.g., Fahey et al.,2001]. PSCs are included based on thermodynamic equilibrium [Carslaw et al., 1995; Hanson and Mauersberger,
1988]. However, PSC formation mechanisms are poorly
understood. As discussed in many studies [e.g., Tolbert,
1994, 1996], NAT does not form at thermodynamic equilibrium, and there is no definite scheme to model NAT
formation. In this study, a control run included STS formation according to the thermodynamic theory of Carslaw etal. [1995]; other runs were performed to examine the effectsof NAT and a possible warm bias in the temperature data
set. Saitoh et al. [2002] found many STS events in midJanuary at altitudes of 20– 23 km and some evidence of
NAT/nitric acid dehydrate (NAD) in early March at altitudesof 16– 18 km in the ILAS aerosol data. We will discuss the

PSC effect on ozone destruction by comparison with thoseresults. A surface-to-mass conversion factor that is based on
size distribution functions measured with an optical particle
counter (OPC) that same winter [Deshler et al., 2000] wasderived to help estimate heterogeneous reaction rates. Mean
radii and standard deviations for STS, NAT, and backgroundsulfate aerosol in lognormal size distribution functions areshown in Table 4. The reaction probabilities (g) for theseven heterogeneous reactions listed in Table 3 were determined for liquid droplets [Hanson, 1998] and NAT[DeMore et al., 1997]. The reaction probabilities for STS
were taken from the data for sulfuric acid droplets because
the data for STS were not available (Tables 3 and 4).

3. Chemical Species Mapping on
Trajectories (CSMT)
3.1. CSMT Scheme[12] CSMT constructs synoptic maps of chemical species
by combining ‘‘trajectory mapping’’ [Morris et al., 1995,
2000] with a photochemical box model. Minor constituentsare time-integrated in a photochemical box model along
trajectories that evolve from satellite measurement points
until a target time. Combining a chemical model withtrajectory mapping allows the creation of synoptic mapsof all long-lived species, and of some short-lived species as
well, such as ClO.
[13] Trajectories in this study were calculated by the
Earth Observation Research Center Trajectory Analysis

Table 2. Photodissociation Reactions Included in the Modela
NumberPhotolysis

(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(20)
(21)
(22)
(23)
(24)
(25)
(26)
(27)
(28)
(29)
(30)
(31)
(32)
(33)
(34)
(35)
(36)
(37)
(38)
(39)
(40)
(41)
(42)
(43)
(44)
(45)
(46)
(47)
(48)

O2 + hn ! 2O
O3 + hn ! O2 + O(1D)O3 + hn ! O2 + O
CH4 + hn ! CH3 + H
CH4 + hn ! CH2 + H2
CH4 + hn ! CH + H2 + H
CH3OOH + hn ! CH3O + OH
CH2O + hn ! H + CHO
CH2O + hn ! H2 + CO
CO2 + hn ! CO + O
H2O + hn ! H + OH
H2O2 + hn ! 2OH
HO2NO2 + hn ! OH + NO3
N2O + hn ! N2 + O(1D)NO + hn ! N + O
NO2 + hn ! NO + O
NO3 + hn ! NO + O2
NO3 + hn ! NO2 + O
N2O5 + hn ! NO + O + NO3
N2O5 + hn ! NO2 + NO3
HNO3 + hn ! OH + NO2
HO2NO2 + hn ! HO2 + NO2
CH3Cl + hn ! Cl + CH3
CH3Br + hn ! Br + CH3
CCl4 + hn ! 4Cl
CH3CCl3 + hn ! 3Cl
CFC11 + hn ! 3Cl
CFC12 + hn ! 2Cl
CFC113 + hn ! 3Cl
CFC114 + hn ! 3Cl
CFC115 + hn ! 3Cl
HCFC22 + hn ! Cl
HCFC142B + hn ! Cl
HAL1211 + hn ! Br + Cl
HAL1301 + hn ! Br
HCl + hn ! H + Cl
ClONO2 + hn ! Cl + NO3
HOCl + hn ! OH + Cl
Cl2O2 + hn ! Cl + ClOO
Cl2 + hn ! 2Cl
ClONO2 + hn ! ClO + NO2
OClO + hn ! ClO + O
BrCl + hn ! Br + Cl
BrO + hn ! Br + O
HBr + hn ! Br + H
HOBr + hn ! Br + O2HBrONO2 + hn ! BrO + NO2
BrONO2 + hn ! Br + NO3

a
Quantum yield of O(1D) by ozone photodissociation (2) is parameterizedfrom Sander et al. [2000].

3-5

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HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

Table 4. Mean Radii and Standard Deviations of Aerosol Size
Distributions Used to Calculate Surface Areas From the Volumesof STS, NAT, and Sulfate (Background) Aerosol
Mean Radius, mm

Standard Deviation

0.29
1.73
0.08

1.45
1.33
1.80

STSa
NATa
Sulfuric acidba

From Deshler et al. [2000].
From Hofmann and Rosen [1984].

bModel (EORC-TAM) [Matsuzono et al., 1998] with meteorological data sets from the European Center for MediumRange Weather Forecasts (ECMWF/TOGA basic level III
data). These data sets include twice a day (0 and 12 UTC) ata horizontal resolution of 2.5 latitude and longitude. Morris
et al. [1995] discussed trajectory dispersion and noted thatmost trajectories were compact before 10 days, even in the
Northern Hemisphere winter. Figure 1 is comparable toMorris et al. [1995, Figure 1], showing the ratio of airparcels (20 km  20 km) that keep megaparcels ‘‘compact’’for the duration of the trajectory when all trajectories arecalculated. Each megaparcel includes one central trajectory
which starts at an ILAS measurement location surroundedby four additional trajectories. The starting points of thefour additional trajectories are 20 km from ILAS measurement location in the north, south, east, and west directions.
A megaparcel is defined as compact if all four trajectories
remain within 400 km of the central trajectory. Figure 1
indicates that trajectories for less than about ten days sustain
enough reliability inside the polar vortex as discussed byMorris et al. [1995]. In this study we used seven-day
trajectories to create maps for chemical species, to maintainreliability in trajectory calculations even during the periodof polar vortex development in January (Figure 1).
[14] Diabatic descent is ignored in the present scheme forseven-day trajectories. Rates of descent estimated from
ILAS N2O data [Kanzawa et al., 2000] and from other data
sources [e.g., Knudsen et al., 1998] are sufficiently small(147 m based on the value from Kanzawa et al. [2000]
and 400 m from Knudsen et al. [1998]) that descent canbe neglected in trajectories covering seven days. Indeed, theobtained CSMT-derived data for each day are from trajectories that span less than seven days.
[15] The initial values of ozone, HNO3, and N2O at theinitial trajectory points are taken from ILAS version
5.20 data. Inversion algorithms used in ILAS version

Table 3. Heterogeneous Reactions and the Reaction Probabilities Used in the Model
Reaction
Probability
(1)
(2)
(3)
(4)
(5)
(6)
(7)
a

Heterogeneous Reaction

Sulfuric Acid

NAT

N2O5 + H2O(aer) ! 2HNO3ClONO2 + HCl ! Cl2 + HNO3ClONO2 + H2O(aer) ! HOCl + HNO3
HOCl + HCl ! Cl2 + H2O (aer)BrONO2 + H2O(aer) ! HOBr + HNO3
HOBr + HCl ! BrCl + H2O (aer)BrONO2 + HCl ! BrCl + HNO30.10a0.30 (192K)c
0.57  102 (192K)c
0.51 (192K)c
0.80 (192K)c
1.00 (192K)c
0.90a

0.40  105b0.20b0.40  102b0.10b0.60  102d
0.10d
0.30d

From DeMore et al. [1997]. g was calculated assuming P = 50 hPa, H2O = 5.0 ppmv, and HCl = 2.0 ppbv.
From Sander et al. [2000].
c
From Hanson [1998].
d
From Lary et al. [1996].
b

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HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

Figure 1. The fraction of air parcels that keep compact
megaparcels for the trajectory duration as a function of timeon the 475 K isentropic surface. Trajectories were calculatedusing ECMWF winds during January to March 1997. Red,
green, and blue lines indicate the fraction of compact
megaparcel trajectories for January, February, and March,
respectively. Solid lines show trajectory calculations at theinside of the inner edge of the polar vortex. Dashed lines
show at the boundary region and at the outside of the outeredge of the polar vortex.
5.20 data are detailed in the work of Yokota et al. [2002],
who reported a root-sum-square total uncertainty of 9% forozone data at 20 km. Ozone data quality in ILAS version
5.20 was validated by Sugita et al. [2002], who compared
ILAS data with many other measurements, and foundagreement to within 10%. Comparisons between balloonborne measurements of HNO3 and ILAS HNO3 data werediscussed by Koike et al. [2000] for version 3.1, and by Irieet al. [2002] for version 5.20. Although ILAS version
5.20 ozone and HNO3 measurements were confirmed in
the latter validation study, ozone can have a large positive
bias of 0.9 ppmv at 20 km when ice PSCs are simultaneously
observed [Yokota et al., 2002]. However, this ozone bias is
unimportant in the present analysis because temperatures did
not reach ice-formation temperatures at ILAS measurement
latitudes in 1997. Ice PSC formation was noted nowhere in
ILAS aerosol measurements [Saitoh et al., 2002] except fora limited area downwind of the Scandinavian mountains in
mid-January [Pan et al., 2002; Saitoh et al., 2002].
[16] The amount of column ozone above the target altitudecontrols the ultraviolet radiation and all photodissociation
coefficients at the target altitude, and was estimated from the
ILAS ozone profile at the starting trajectory point. Total
chlorine and bromine at 475 K were set to 3.0 ppbv, and20.0 pptv, respectively, values from a recent WMO report[WMO, 1999].
[17] Initial values of other species were taken from astatistical analysis of calculations on several ‘‘representative
trajectories,’’ a method similar to the ‘‘idealized trajectory’’of Becker et al. [1998]. Thirty-six representative trajectories
were calculated from 25 December 1996 to 31 May 1997, at
10 longitude intervals with starting points at latitude 75N.
Initial values of the chemical calculations on representative
trajectories for CH4 and H2O were taken from the averaged
values of the Halogen Occultation Experiment (HALOE)
data (version 19) observed in the polar vortex from March

to April 1997. The initial values of other species were takenfrom the output of a one-dimensional model that was
supplied by Susan Solomon at the NOAA Aeronomy
Laboratory. The ‘‘representative initial values’’ for all
short-lived species were obtained from calculations for the
representative trajectories using output every 6 hours. Outputs of short-lived species were smoothed with a four-day
running mean on each trajectory, and the output from the36 trajectories were averaged for each day north of 60N.
The data set obtained was set as the representative initialvalue for later calculations.
[18] Additional time integrations adjusted short-lived
species to the atmospheric conditions at the starting points.
Trajectories for seven days are calculated from each ILAS
observation point from January to March. On all trajectories, the photochemical box model calculates the time
evolution of the chemical species. All chemical species
calculated for a target day were combined to create asynoptic map. Figure 2 shows examples of CSMT-derived
maps of Arctic ozone on the 475 K isentropic surface forevery 10 days from January through March. Mixing ratios
were averaged if more than one data value occupied thesame 2.5  2.5 grid box. Polar vortex boundaries were
determined by a method similar to that of Nash et al. [1996]
and archived at the ILAS/Data Handling Facility (DHF).
The three solid lines in each panel represent the inner edgeof the vortex boundary, the peak vortex edge, and the outeredge of the vortex boundary. Blank areas represent no data
for mapping. ILAS was an occultation sensor on a polarorbiting satellite; latitudinal coverage was limited at highlatitudes near 65– 70N. Nevertheless, the CSMT map withseven-days trajectory calculations covers most of the polar
vortex, with a lack of coverage only near the Pole. In
January, ozone mixing ratios show uniformly high valuesof 3.2 ppmv in the polar vortex. Ozone decreased gradually after mid-February and reached lower values in Marchin the polar vortex. The distribution of the ozone mixing
ratio is not uniform, with lower values at high latitudes inlate March (Figure 2).
[19] As described in section 2, PSCs are formed as the aircools along trajectories in the model. The total amounts of
HNO3 and H2O in gas-phase and particles needed for
calculations on the thermodynamic growth of PSCs were
estimated from ILAS HNO3 and H2O data. Although ILAS
measured HNO3 and H2O along with aerosols, data thatwere colocated with PSCs were not used to determine thethreshold temperature of PSCs’ formation because where
PSCs exist there could be a significant bias in the data
[Yokota et al., 2002]. A background average was usedinstead. HNO3 and H2O data inside the polar vortex wereaveraged for each 10-day period in the region where thecolocated temperature exceeded 200 K. The average values
were taken as the total background amounts including gas
and particles of HNO3 and H2O [Hayashida et al., 2000b;
Saitoh et al., 2002].
[20] To judge the activation of chlorine species at thestarting point, results from PSC analysis of ILAS data areutilized [Saitoh et al., 2002]. When a PSC event wasobserved at the starting point, enhancement of Cl2, HOCl,
HOBr, and BrCl up to 0.4 ppbv, 0.4 ppbv, 5.0 pptv, and10.0 pptv, respectively, was assumed, which may result in
about 1.2 ppbv of ClOx and about 15.0 pptv BrOx underHAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

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3-7

Figure 2. Time series of CSMT maps of ozone obtained every 10 days from January to March in 1997.
Colors indicate ozone mixing ratios on the 475 K isentropic surface.
sunlit conditions. However, this assumption may cause
underestimation of chlorine activation because PSC might
have been formed just before the ILAS measurement timeeven if ILAS could not observe it. To incorporate thetemperature history experienced by air parcels before thetrajectory started, backward trajectories were also calculated
at all starting points to determine temperature change. Whenthe minimum temperature along the 10-day backward
trajectory was less than 195 K, same enhancement of chlorine

and bromine are assumed. Though 10-day may be too long toinvestigate the low temperature experience, there was notessential difference if the period was fixed to three days.
3.2. Comparison of CSMT Ozone With
Ozonesonde Data[21] The ILAS project conducted validation campaigns
including ozonesonde soundings at Andoya (69.3N,
16.1W), Fairbanks (64.9N, 147.9E), Kiruna (67.9N,

ACH

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HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

tories from ILAS measurement points at q = 475 K, 550 K,
700 K, and 750 K. All ozone data (and other species data) athourly intervals are stored along with trajectory information. For one specific ozonesonde measurement, all isentropic trajectories located within a circle of 400-km radius
centered on the sonde measurement location with a timedifference of ±12 hours were selected. The nearest point for

Figure 3. Schematic diagram for selection of the pair ofozone data derived from CSMT and observed withozonesonde. The CSMT trajectories in a circle of 400 km
radius centered on the sonde measurement within ±12 hours
were collected. For each trajectory, the CSMT ozone data
nearest to the sonde measurement was selected as indicatedby gray arrows. The ozone data selected for all suchtrajectories in the circle are averaged and compared with theozonesonde data.
21.1W), and Yakutsk (62.0N, 129.6W) for the Northern
Hemisphere. These ozonesonde data are archived as ILAS
Correlative Measurement Data Base (ILAS-CMDB). Sugita
et al. [2002] compared ILAS version 5.20 ozone data withthem through the ‘‘traditional approach’’ rather than a
Lagrangian approach using the Trajectory Hunting Technique as proposed by Danilin et al. [2002] because they
could find many coincident measurement pairs through theconventional method. They indicated that the ILAS andozonesonde data agree within 10% between 13 and 30 km,
and at the q  475 K level used in this study, the differenceis even less than 5% [see Sugita et al., 2002, Figure 5a].
The ozonesonde is well-validated and accepted as a precise
sensor, so the consistency of ILAS and ozonesonde data
underscores the reliability of ILAS version 5.20 ozone data.
In the following, we show the comparison of ILAS andozonesonde measurements thorough a Lagrangian approach
as Danilin et al. [2002] applied to the comparison ofdifferent satellite sensors. This is not to validate ILAS
ozone data but to validate the CSMT scheme and its
components (the trajectory tool, the meteorological dataset, and the chemical box model) used in this study.
[22] We used ozonesonde profiles at Kiruna (51 profiles)
and Yakutsk (11 profiles), all of which are archived in the
ILAS-CMDB. In the first approach, ILAS and ozonesondepairs were selected such that time and space differenceswere limited to ±12 hours and 400 km, respectively. The
relative differencein the two-paired data was defined as
ILAS
Ozonesondefor each pair, and D1 values for all
D1 ¼ OzoneOzone
sondepairs were averaged (D1 ). Next, we applied trajectory
mapping. Ozone mixing ratios are calculated along trajec-

Figure 4. (a) Scatterplot of ozonesonde data versus ILAS
or CSMT ozone data at 475, 550, 700, and 750 K. Diamondindicates ILAS versus ozonesonde data selected by the
traditional approach. Asterisk shows CSMT ozone versus
ozonesonde data selected by the Lagrangian approach. The
solid line is the one-to-one line. The averaged error bars ofozonesonde and ILAS data are shown on the one-to-one linewith a solid circle. The errors for ILAS and ozonesonde data
are the averaged value of each error of the ILAS and sondedata, respectively. Error of ozonesonde data is taken 5% of
the value. (b) Relative difference between ozonesonde and
ILAS or CSMT ozone at 475, 550, 700, and 750 K. The
solid line indicates relative difference between CSMT andozonesonde data, that is D2 . The dashed line indicates
relative difference between ILAS and ozonesonde data, thatis D1 . The maximum and minimum values of D1 and D2 arealso shown.

HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

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3-9

Figure 5. (a) Map of 475 K CSMT ozone ( ppmv) on 28 January 1997. In this projection, 0 longitude isat the bottom and the black circles are for 30N and 60N. All points are the ozone-mixing ratio (color
code is shown in the bar under the map) calculated for 28 January 1997 using about 90 trajectories
starting from ILAS measurement points from 21 to 28 January. The blank area represents no data. The
three solid lines are the inner boundary, the peak edge, and the outer boundary of the polar vortex
provided by ILAS/DHF. (b) The 475 K MLS version 5 ozone ( ppmv) on 28 January 1997.
one trajectory was taken as comparable data for the target
ozonesonde data. Figure 3 conceptually shows this method.
For most cases, about 6  7 trajectories can be taken for one
target sonde measurement, and the nearest point to the target
sonde data was taken for each trajectory. The averaged
value for those data was defined as the counterpart ofthe ozonesonde data, and compared in the same way asin the conventional comparison. We can determine
sonde
and D2 in the same way with D1
D2 ¼ OzoneCSMT Ozone
Ozonesondeand D1 (Figure 3).
[23] Figure 4a presents the scatterplot of ozonesonde data
versus ILAS or CSMT ozone data. Diamonds indicate
original ILAS data selected using the traditional approach,and asterisks indicate the Lagrangian approach. CSMT data
show better correlation to sonde data than ILAS data, whichreflects the reliability of trajectory calculations. Unlike
Trajectory Mapping (TM), CSMT can provide ozone dataat photochemically active altitudes such as 750 K. Figure 4b
depicts the relative difference (%) between ozonesonde data
and ILAS or CSMT, that is, D1 and D2 . The D2 values arecomparable to D1 , and minimum value of D2 is much larger,
which suggests the mapping preserves the reliability of theoriginal ILAS data even after seven days’ integration along
the trajectories. This suggests the ozone map shown in
Figure 2 is reliable, and highlights the accuracy of the
CSMT scheme used in this study (Figure 4).
3.3. Comparison of CSMT Maps With MLS Data
3.3.1. Ozone[24] Figure 5a shows a representative CSMT ozone map
for 28 January, before a period of significant chemicalozone destruction in late February and March. Manney etal. [1997, Figure 1] shows the Microwave Limb Sounder(MLS) version 4 ozone data at 475 K for the same day.
Though the CSMT ozone data have slightly higher values
than MLS version 4 data in the work of Manney et al.
[1997], the recent algorithm of MLS (Version 5) haveremoved the bias that was in version 4, increasing globally
averaged ozone values by about 0.6 ppmv at 46 hPa (MLS
version 5 readme file). Figure 5b depicts version 5 MLS
data observed on the same day as Figure 5a. Figures 6a and6b also show the CSMT and MLS version 5 ozone maps
observed on 26 February during the significant ozone loss
period. There are consistent features in ozone distributionsin the CSMT and MLS ozone maps for both 28 January and
26 February as indicated in those figures (Figures 5 and 6).
[25] To compare CSMT ozone values with MLS version 5
data more quantitatively, we applied an approach similar tothat used for the ozonesonde comparisons. Original ILAS
data and CSMT-derived ozone data within ±12 hours and
400-km were compared to MLS data. Figure 7a is ascatterplot of original MLS versus ILAS data in late January
1997. Red, green, and blue triangles indicate the inside,boundary, and outside of the vortex, respectively. Most of
the data selected are consistent within the error bars, thoughsome data are not. More data pairs in the CSMT scheme canbe compared with MLS data. Figure 7b superimposes
CSMT ozone on Figure 7a. The colored dots in Figure 7b
are the scatterplots of MLS ozone and the CSMT-derived
ozone that correspond to each MLS data point by averaging
6  7 data points as in the comparison with ozonesondedata. Some of the ILAS data do not correlate with theACH

3 - 10HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

Figure 6. (a) As in Figure 5a but for 26 February. (b) As in Figure 5b but for 26 February.
corresponding MLS data. CSMT-derived ozone initialized
by those ILAS data inevitably diverges from the MLS data,
as represented by the scattering of colored points aroundthose data, but that is a limit of this scheme. However, mostof the CSMT data are within the error range, showing thecorrelation between CSMT and MLS data. This is further
proof of the robustness of the CSMT scheme. Figures 7cand 7d are similar scatterplots for late February. As shownin the figure, most of the points are again in the range oferror bars. Though some data points are scattering at far
outside of the error range, they are all for outside of thepolar vortex, therefore, there is not a significant effecton later analysis of chemical processes inside the vortex.
Table 5 summarizes some statistical values for the relationof MLS and CSMT data for all data points shown in
Figures 7b and 7d. The correlation coefficient for late
February is not efficient enough but this is due to thelimited data range, and the root-mean square (RMS)
difference indicates enough reliability of the CSMT data.
Table 5 also demonstrates the CSMT scheme does notderive any systematic bias (Table 5 and Figure 7).
3.3.2. Nitric Acid[26] Figure 8a shows CSMT HNO3 for 28 January. The
comparable MLS (version 4) data at 465 K on the same day
are presented in the work of Santee et al. [1997]. Santee etal. [1997, Figures 8a and 2] depict HNO3 mixing ratios ashigh as 12 ppbv almost everywhere within the polar
vortex. Figures 8b depicts version 5 MLS data at 475 K
on the same day as Figure 8a, which again indicates thegood correlation of the two maps. Figure 9a shows CSMTHNO3 for 20 February. This is comparable to the MLS
HNO3 (version 4) for the same day as in the work of Santee
et al. [1997, Figure 2]. Figure 9b depicts version 5 MLS
data at 475 K on the same day as in Figure 8b. The notabledecrease in CSMT HNO3 to 2 ppbv (shown in violet)
deep inside the vortex, which is associated with low temper-

atures and uptake into PSCs in the model, agrees with the
low nitric acid values in the MLS map for the same day(Figures 8 and 9).
[27] We now compare the CSMT HNO3 values with MLS
version 5 data more quantitatively. Figure 10 is similar to
Figure 7 but for nitric acid. Figures 10a and 10c depict
scatterplots of the original MLS and ILAS data in late
January and late February, respectively. Figures 10b and10d superimpose CSMT HNO3 on Figures 10a and 10c,
which highlight the good correlation of CSMT-derived
nitric acid data with MLS version 5 data. Though theprecision of MLS version 5 data at 46 hPa is 1 ppbv
(MLS version 5 readme file), the one-to-one correlationline should be shifted to the right in the figure because
Danilin et al. [2002] reported that the MLS version 5 data
were larger than the original ILAS version 5.20 data by
1 ppbv near 475 K. Most of the scattered data are within
the range of the error bars, although some of the original
ILAS data lie outside the range. CSMT-derived data initialized by those ILAS data are inevitably outside the range ofthe error as discussed on ozone. Nevertheless, the goodcorrelation observed in Figure 10b demonstrates how well
CSMT nitric acid data match MLS data. For late February,
the nitric acid data inside the vortex scatter in wide range.
Some nitric acid data indicate very low values as in
Figure 10d because significant denitrification occurred
during the period [Kondo et al., 2000]. As described in
the previous section, we can reproduce those denitrified
situations even without a sophisticated model of PSC
growth and sedimentation because denitrification is involved implicitly in our analysis by initializing chemical
model with the really observed nitric acid. This result again
demonstrates the advantage of CSMT (Figure 10).
3.3.3. Chlorine Monoxide[ 28 ] Minor constituents, including radicals, can bederived in the same way, but not all are presented here.

HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

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3 - 11Figure 7. (a) Scatterplot of ILAS and MLS ozone for data on 28, 29, 31 January and 1 and 2 February at
475 K. Triangle points show the scatterplots for the original MLS and ILAS data within the criteria of
400 km and ±12 hours. Red, green, and blue dots indicate the data at the inside, boundary, and outside thevortex, respectively. Dashed line indicates one-to-one line, and error bars of ILAS or MLS data are also
shown on the line with a solid circle. The errors are estimated by averaging ILAS or MLS errors in data.
(b) Superimposition of CSMT ozone on Figure 7a. The colored dots show the scatterplots for MLS andCSMT data within the same criteria. The scatterplots of original MLS and ILAS data are also shown as
black triangles. (c and d) As in Figures 7a and 7b but for data on 20, 21, 23, 24, and 26 February at 475 K.
Many of the radical species have no observational counterpart with which to compare, but ClO data are available fromMLS.
[29] Validation of the version 3 ClO data from MLS wasreported by Waters et al. [1996]. The accuracy andprecision of version 4 MLS data at 46 hPa were 0.4 ppbv
and 0.2 ppbv, respectively. Figure 11 shows CSMT ClO on
20 February; ClO data are plotted only in sunlit regions.
Thick solid lines represent 10 hours (outer line) and 5 hours(inner line) of sunlight each day. The thin line indicatesthe vortex edge. The CSMT map shows ClO strongly
activated in the polar vortex at around 120– 135E andTable 5. Summary of the Comparison of MLS and CSMTDerived Ozone Data
Late January
ILAS

CSMT-Derived

Number of the data
101
852
Correlation coefficient 0.74
0.67
15.22
11.24
RMS, a %12.54
7.40
Bias, b %p
ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ
ﬃNPðILASi ðCSMTi ÞMLSi Þ2a1
RMS = 100  N
.
MLSi
N
i¼1 P
ðILASi ðCSMTi ÞMLSi ÞbBias() = 100  N1
.
MLSi
i¼1

Late February
ILAS

CSMT-Derived

88
0.51
9.86
6.91

909
0.50
9.17
4.90

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HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

Figure 8. (a) Map of 475 K CSMT HNO3 (ppbv) on 28 January 1997. (b) The 475 K MLS version 5
HNO3 ( ppbv) on 28 January 1997.
40– 80W at 60N; compare this to the version 4 ClO in
the work of Santee et al. [1997, Figure 2] for the same day,with the same sunlit condition. Observation times must be
adjusted to compare the ClO map from CSMT with thatfrom MLS because ClO has a large diurnal variation. The
two maps are nevertheless comparable, even without strict
adjustment of the observation time because the sunlit area
is limited to the same longitudes at a given time, and thesunlit regions for both CSMT and MLS are consistent.
The ClO activation area as in the work of Santee et al.
[1997, Figure 2] is consistent with the CSMT-derived ClO
distribution, even though ClO activation in CSMT is lessthan MLS ClO. Part of the quantitative difference is due tothe bias in MLS version 4 data. Version 5 of the MLS
algorithm yields less ClO at 46 hPa than version 4(Figure 11).

Figure 9. (a) As in Figure 8a but for 20 February. (b) As in Figure 8b but for 20 February.

HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

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3 - 13Figure 10. (a) Same as Figure 7 but for HNO3. (b) Superimposition of MLS and CSMT HNO3 on
Figure 10a. (c and d) As in Figures 10a and 10b but for data on 20, 21, 23, 24, and 26 February.
[30] Because ClO has a large diurnal variation, a different
approach for comparing MLS and CSMT ClO data in thesame photochemical situation (same local time)
was adopted. The time difference criteria for the comparison
were greatly reduced, and the spatial criteria were relaxed.
We selected 18 circles of 1000-km radius along 75N
latitude at 20 intervals to compare. The 75N latitudinalband was chosen because most of the area falls inside thevortex. In addition, that latitude yields enough CSMT data
for a valid comparison. All MLS data (usually about three)
and CSMT data (typically six to seven) that fell within the
1000-km radius disk and had times that were within an hourof each other were averaged and compared. The criteria ofan hour and 1000-km distance are similar to values used in a
NO2 validation study [Irie et al., 2002].
[31] Figure 12 is a scatterplot for the ClO data. The
accuracy and precision of version 5 of the MLS data at

46 hPa are estimated as 0.2 ppbv and 0.3 ppbv, respectively.
Although CSMT values are somewhat smaller than MLS
values, ClOx activations at 1.5 ppbv in CSMT are consistent quantitatively with those in simulations by a chemical transport model inside the vortex in late February 1997
at q = 475 K [Chipperfield and Pyle, 1998]. Because apositive bias of 0.09 ppbv exists at 46 hPa in MLS version 5
ClO (MLS version 5 readme file), the one-to-one correlationline can be shifted to the right as shown by a solid line in the
figure. Most of the data pairs are consistent with the solidline given the large MLS error range. This consistency
between the two data sets verifies the reasonable ClO
activation produced by the model. However, there is a still
systematic underestimate in ClO derived from CSMTscheme for the cases of high chlorine activation, which
may cause less ozone destruction in the model thanobserved. Danilin et al. [2000] and van den Broek et al.

ACH

3 - 14

HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

Figure 11. Map of 475 K CSMT ClO (ppbv) on
20 February 1997. Only data during sunlit hours were used
for mapping. Thick solid lines indicate five hours and ten
hours of sunlight in a day. The solid lines are the boundariesof the polar vortex as in Figure 5a.
[2000] also reported similar underestimation of ClO relative
to MLS data. This issue is discussed in the next section
again (Figure 12).

Figure 12. Scatterplot of MLS and CSMT ClO for thedata on 20, 21, 23, 24, and 26 February at 475 K. Dottedline shows one-to-one correlation. Averaged error of MLS
data is also shown. Solid line indicates one-to-one
correlation after subtracting known bias value of 0.09 ppbv
at 46 hPa in MLS version 5.
the inner edge of the vortex boundary. Dots in the figure are
the daily averaged latitudes of air parcels. About 30 –40 data points were available every day. Scatter of the dots
in Figure 13 is restricted to between 85– 55N, reflecting thelatitude range of ILAS measurements. The dots extend tolower latitudes near 50 in mid-January following the vortex
distortion as noted above. Color indicates the minimum

4. Discussion
4.1. Arctic Ozone in the Late Winter / Spring of 1997[32] Because it controls photochemical processes, solar
zenith angle, which is linked in a straightforward manner togeographical latitude and season, is treated carefully in thepresent study. The focus is the temporal evolution ofchemical processes in the Arctic in the winter of 1997 as
a function of changes in the solar zenith angle, rather than as
a function of other dynamical parameters, such as equivalent latitudes or relative potential vorticity. Classificationaccording to geographical latitude is not essentially different
from that of equivalent latitudes for a symmetric polar
vortex. The Arctic in the winter of 1997 was dominatedby a strong symmetric vortex beginning in late February[Coy et al., 1997] and persisting through early spring.
Therefore geographical latitude in March 1997 was better
correlated to relative location in the polar vortex than inother Arctic winters [Guirlet et al., 2000]. In January 1997,
however, the vortex deformed significantly, and parts of it
were exposed to strong solar radiation that enhanced ClO
levels in some areas.
[33] Figure 13 depicts the latitudinal coverage of airparcels that were advected from the ILAS measurement
points. All initial points (ILAS measurements) were inside

Figure 13. Latitudinal distribution of the air parcels foreach day. Colored dots show the locations of air parcels on
respective trajectories for each day. The minimum solar
zenith angle each day is indicated with a color code. Black
dots indicate the latitude of ILAS measurement points.

HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

ACH

3 - 15solar zenith angle experienced by an air parcel in a day. The
continuous decrease in solar zenith angle after late February
reflects the seasonal change (Figure 13).
[34] Figure 14 shows time series of the ozone mixing
ratios calculated for the points in Figure 13. Dots depict theozone mixing ratio at 475 K at 0000 UTC for each day as
obtained from CSMT calculations with starting points inside
the inner edge of the vortex boundary. Mean values,
averaged for all data for every day, are connected with asolid line. The figure shows that the average ozone mixing
ratio was nearly constant from January through mid-February before starting to decline in mid-February and thencontinuously decreasing until the end of March. There wasconsiderable scatter in ozone mixing ratios in March,suggesting nonuniform ozone distribution in the polar
vortex associated with chemical ozone changes and dynamical mixing (Figure 14).
4.2. Chemical Ozone Loss Rate From January
Through March 1997[35] Figure 15a shows time series of chemical ozone loss
rates ( ppbv/day) estimated from 24-hour differences inozone mixing ratios along trajectories in the chemical box
model. In contrast to the time variation of the ozone mixing
ratios in Figure 14 that include dynamical effects, the ozone
loss rates shown in Figure 15 are attributed to chemicalchange alone because they are determined from model
calculations. Figure 15a is for a control run, assuming
STS formation. As in Figure 13, color indicates the minimum solar zenith angle during the 24 hours of integration ofeach day for each trajectory. Daily mean values are shown

Figure 14. Time series of the calculated ozone by CSMT
on 475 K at about 0000 UTC for each day. All of the initialpoints of trajectories (ILAS observation points) were taken
inside the polar vortex. Information on the vortex edge wasprovided by ILAS/DHF, based on UKMO meteorological
data using the approach of Nash et al. [1996]. Dots indicate
the ozone-mixing ratios calculated for the target day along
different trajectories. The color indicates the minimum
value of the solar zenith angle experienced during eachtrajectory. The thick solid line is the average.

Figure 15. (a) Calculated ozone loss rates in ppbv/day for
the STS case. Ozone decrease is taken to be positive. Eachdot indicates the ozone loss rate calculated for the target day
along different trajectories. Ozone loss was estimated fromthe difference between the first calculated output of thetarget day and the value 24 hours later. The color indicatesthe minimum value of the solar zenith angle experienced
during each calculation. The thick solid line is the average.
The numbers of STS (red) and NAT/NAD (blue) events
observed at an altitude of 475 ± 25 K with ILAS [Saitoh etal., 2002] are indicated in the figure. (b) Same as Figure 15a
but for the lower temperature case. (c) Same as Figure 15a
but for the NAT case.

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HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

by a solid line. The mean ozone loss rates were positive(ozone decreasing) throughout January, with small peaks on
19, 26, and 29 January. Mean ozone loss increased gradually
toward late February, and exceeded 20 ppbv/day for 31
consecutive days. The maximum rate (black solid line) was
34 ± 10 (mean ± one-s value) ppbv/day on 28 February. The
mean ozone loss rate slowly decreased throughout March.
[36] Knudsen [1996] reported that ECMWF temperatures
have a warm bias of about 1.4 K near 475 K. The effect of
this warm bias for the case of STS formation is shown in
Figure 15b. For these calculations, all temperatures are
initially 1.4 K colder than the control run. As a result, asshown in the figure, ozone decreases more rapidly. The
maximum loss is 38 ± 13 ppbv/day on 28 February. No
significant difference in average ozone loss rates exists
between the control run and the warm bias case. However,relatively large increases of ozone loss rates occur around
20 January and from early February to late March. Minimum temperatures along trajectories in this study are almost
equivalent to minimum temperatures over the polar region
as reported by Chipperfield and Pyle [1998, Plate 1]. The
temperatures are between the STS formation temperature(192 K) (TSTS) and 1.4 K higher (TSTS < T < TSTS+1.4 K)
from 15 to 25 January, from 5 February to 10 March, andfrom 17 to 20 March. The increase in the surface area due to
STS formation is significant in those periods when thewarm bias of 1.4 K is considered. Many of the reaction
probabilities for heterogeneous reactions increase nonlinearly with temperature and are more sensitive to colder
temperatures. Thus reaction probabilities increase when
original temperatures are as low as those accompanying
STS formation. The heterogeneous reaction rate is determined by the product of aerosol surface area and reaction
probability (g), so the increase in heterogeneous reactions
rates is significant in the periods mentioned above, followedby an increase in ozone loss.
[37] Figure 15c shows the results if NAT formation isassumed instead of STS formation. The time series of ozone
loss rate is similar to that in Figure 15a, although ozone
decreases more for all periods. The maximum rate ofozone loss is 37 ± 11 ppbv/day on 28 February. The
increased ozone loss rates for NAT do not differ significantly from those for STS, but the increases are relatively
large in the second half of January and from early February
to late March, similar to the warm bias case. Larger ozone
loss rates in this case are linked to differences in PSC
growth and reaction probabilities between NAT and STS.
The temperature threshold of particle formation is about 3 K
higher for NAT than for STS (around 195 K), and theenhancement of PSC surface area is significant. Although
reaction probabilities (g) for NAT for all heterogeneous
reactions are not always larger than those of STS, resultant
heterogeneous reaction rates for NAT are larger than thosefor STS. Therefore the ozone loss rates are again enhancedin those periods, as in the warm bias case.
[38] The numbers of STS (red) and NAT/NAD (blue)
events observed with ILAS at an altitude of 475 ± 25 K areshown in Figures 15a– 15c, which was adapted from Saitoh
et al. [2002]. Some NAT/NAD events (blue) were observedin mid-January, mid-February, and mid-March. In these
periods, the ozone loss rate would be larger than the loss
in the control run. However, ozone loss rate does not

Figure 16. Dependence on latitude of the ozone loss ratein ppbv/day. The four lines are: (solid) averaged for all
latitudes, (dotted) 80 – 90N, (dashed) 70 – 80N, and(dashed and dotted) latitudes equatorward of 70N.
depend significantly on PSC type, as shown in Figures 15a
and 15c; the integrated ozone loss for NAT for all the
periods is only 5% larger than that for STS case. PSC
growth mechanisms are poorly understood, and manysophisticated PSC growth theories are now being investigated. Nevertheless, ozone losses calculated using thethermodynamic theory of PSC growth are reasonable inmany simulations. Our estimated ozone loss rates are within
an acceptable range, despite shortcomings in the PSC
formation model (Figure 15).
[39] As noted above, ozone loss rates are sensitive to solar
radiation, so latitude or solar zenith angle must be consideredwhen these results are compared to other studies. Figure 16
shows a clear latitudinal dependence in the rate of chemicalozone loss. Ozone loss occurred mainly at lower latitudes
until late February. In March, the region of significant ozone
loss shifted to higher latitudes, consistent with other studies[e.g., Chipperfield and Pyle, 1998] (Figure 16).
[40] Figure 17 shows the ratio of the integrated ozone
loss relative to the initial ozone mixing ratio on 13 January,which is comparable to Chipperfield and Pyle [1998,
Figure 1]. Integrated ozone loss was 41% when averaged
within the inner edge of the polar vortex. Integrated ozone
loss amount depends on latitude as shown in Figure 16
because of inhomogeneities in ozone loss rates in the polar
vortex. To explain chemical processes in ozone destruction,
the time series of the representative partitioning (HCl,ClONO2, and ClOx (= Cl + ClO + 2Cl2O2)) of total
inorganic chlorine (Cly) for the control run are investigated.
Mixing ratios of HCl and ClONO2 decreased in late January
and in late February as ClOx increased, suggesting a
conversion from inactive chlorine to active chlorine during
those periods. The small enhancement of ClOx that occurredin late January was affected by activation under relatively
strong solar radiation at the edge of the vortex, when it
deformed to lower latitudes after PSCs formed at low
temperatures. The maximum ClO mixing ratio, about
1.6 ppbv, occurred in late February that corresponds toperiods of significant chemical ozone depletion. Though

HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

Figure 17. The rate of ozone loss as a relative percentageof the ozone-mixing ratio on 13 January 1997. The fourlines are the same meaning with Figure 16.
this value is almost consistent with the estimate from otherstudies [e.g., Rex et al., 2003], CSMT cannot reproduce
high ozone loss in January nor high activation of ClOx as
discussed by Rex et al. [2003]. Here our analysis in basedon only observed ozone and nitric acid, and no realistic
amount of chlorine species is included. To improve thedeficit in active chlorine, we need more information on
observed chlorine species (Figure 17).
[41] Figure 18 shows the time series of the HNO3
calculated by CSMT on 475 K at about 0000 UTC for eachday, as done for ozone in Figure 14. Initial trajectory points
lie inside the polar vortex. Dots indicate the HNO3 mixing
ratio calculated for the target day along different trajectories. The thick solid line depicts the daily mean, and color
indicates the minimum SZA. Some HNO3 measurements
are significantly lower in late February. Kondo et al. [2000]
and Irie et al. [2001] reported that denitrification started justafter 10 February at altitudes of 17– 21 km. Irie et al.
[2001] calculated a significant denitrification (43%) from
ILAS HNO3 data at 19 km (near 475 K) in late February.
Because the significant denitrification in late February
would cause low mixing ratios of HNO3 at q = 475 K thatwould persist, the denitrification should have influenced
ozone recovery in the spring of 1997 as a consequence of
prolonged ClOx activation. In our model, the calculations
described above implicitly involve denitrification because
denitrified gaseous HNO3 data were used as initial values.
However, the net effect of denitrification is difficult todetermine because denitrification enhances Cly-catalyzed
ozone loss but suppresses NOx-catalyzed ozone loss. Analysis to distinguish the effect of denitrification is ongoing(Figure 18).
4.3. Estimate of the Effect With / Without
Diabatic Cooling[42] In the model calculations, ‘‘representative initialvalues’’ initialize the chemical species other than the observed species, as described in section 3.1. The data set of‘‘representative initial values’’ were obtained from model
calculations of 36 air parcels along ‘‘representative trajec-

ACH

3 - 17

tories’’ for 90 days on the same isentropic surface, such as
475 K without diabatic descent. As reported by Knudsen etal. [1998], the air mass at 475 K on 10 April, 1997 was at
590 K on 1 January, 1997, and the effect of diabatic descent
might have caused a significant error in the initial values,
leading to a considerable bias in the calculated results. We
estimated the amount of possible error in the calculated
ozone loss rates. Calculations along diabatic descending
trajectories as in the work of Knudsen et al. [1998, Figure 1]
are very complicated because of the adjustments needed forpressure, temperature, air number density, and J-values.
Here we use an alternative approach to estimate the error
for representative initial values. We calculated ‘‘initial
representative values’’ at 550 K, and applied them to CSMT
calculations at 475 K, and the results were compared withthe control run shown in Figure 15a. This error estimate willbe the upper limit of the actual error, as trajectories should
not remain at 550 K for 90 days. The ozone loss rates at
475 K obtained by incorporating initial values from 550 K
were not different qualitatively from those in the control
run: both show a continuous ozone decrease from January
to March with a peak in late February. ‘‘Representative
initial values’’ do not substantially influence the results
because ozone and nitric acid (therefore NOy) are constrained from measurements, and Cly and Bry are fixed.
Therefore possible errors in the results associated with thepoorly determined initial values would not be large, eventhough the diabatic descent rate may be significant. In thepresent example, the integrated ozone loss at 475 K using
initial values at 550 K from January to March is 25% lower
than the ozone loss in the control run. However, the effectestimated here is the upper limit of the possible error.
Nevertheless, quantitative estimates of the integrated ozone
loss presented here must be carefully interpreted. Work to

Figure 18. Time series of the calculated HNO3 by CSMT
on 475 K at about 0000 UTC for each day. All of the initialpoints of trajectories were inside the polar vortex, as in
Figure 13. Dots indicate the HNO3 mixing ratio calculated
for the target day along different trajectories. The thick solidline depicts the average.

ACH

3 - 18HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

yield more realistic initial values is ongoing, and results will
be reported in the near future.
4.4. Comparison With Other Studies[43] In this section, present results are compared to otherstudies. Several studies used the SLIMCAT CTM to investigate the 1997 Arctic winter. Chipperfield and Pyle [1998]
reported an ozone loss of 23% (relative to 1 January) in the
Arctic winter of 1997 at 475 K for the area north of 60N.
Hansen and Chipperfield [1999] derived a similar ozone
loss within the Arctic polar vortex at 475 K relative to
1 February. Guirlet et al. [2000] derived an ozone loss of
36% at 480 K north of 75N equivalent latitude relative to
20 December 1996. Ozone loss rates depend on the locationin the vortex as discussed above, so comparison of relativeozone loss amounts in various studies is not straightforward
without a careful adjustment of the target region and theperiod. Nevertheless, our result is consistent with otherstudies based on SLIMCAT (compare Figure 15 with
Chipperfield and Pyle [1998, Figure 1]), and an adjustmentof the period decreases the difference between the studies.
For example, the integrated ozone loss in the present study
decreases to 33% when the integration period is set from
1 February to 31 March to match more closely the values in
the work of Hansen and Chipperfield [1999]. Furthermore,
the continuous increase in the integrated ozone loss with thelargest gradient in late February in this study (see Figure 15)
is similar to features in the work of Chipperfield and Pyle
[1998], Hansen and Chipperfield [1999], and Guirlet etal. [2000], suggesting the reliability of the simulations
presented here.
[44] Schulz et al. [2000] derived a maximum ozone loss rateof 40 ppbv/day at day number 60 in 1997 at 475 K inside thevortex, based on a ‘‘Match’’ analysis using sonde measurements. Chemical ozone loss rates estimated by Match inside
the vortex in February are in good agreement quantitatively
with our study. However, ozone loss rates in this study
gradually decrease from late February to March, while ozone
loss rates in the work of Schulz et al. [2000] drop from a highof 40 ppbv in early March to a lower value in late March.
This difference in ozone loss rates may reflect a change in therelative position in the polar vortex. The Match analysis in thesecond half of March does not represent the averaged condition in the polar vortex because only a few Match-pairs weretaken at the vortex core [Schulz et al., 2000]. Although the
maximum ozone loss rates at day number 60 by Schulz et al.
[2000] are greater than values computed in this study forinside the vortex (30 ± 12 ppbv/day), the two results doagree within 1-sigma value. Manney et al. [1997] estimated achemical ozone loss rate of 1.3%/day using MLS data
observed from 20 February through 26 February at 465 K,
which corresponds to about 30 ppbv/day, consistent withestimates in this study of 30 ± 12 ppbv/day.
[45] As mentioned in the introduction, Becker et al. [1998,
2000] compared ozone loss derived from a Match analysis
with that calculated using a chemical box model along thesame trajectory as the Match pair in the Arctic winter andspring of 1992 and 1995. They found that the model underestimated ozone loss by Match in January, and suggested
there should be an important unresolved issue in polar ozone
chemistry. This issue is still being debated [e.g., Rex et al.,2003]. Recently, Terao et al. [2002] applied Match techniques

to ILAS data. They showed a maximum ozone loss of 60 ± 10
ppbv/day at 475 K in early March, a much larger loss than thatin this study. However, their results cannot be interpreted as anoverestimate of chemical ozone loss in measured data versus
model predictions because Terao’s estimate is even larger
than the sonde-Match analysis by Schulz et al. [2000].
[46] To compare our estimate with the ozone loss derived
by the Match technique based on ILAS measurement by
Terao et al. [2002], we need to take several differences into
account. First, Match analysis is more limited in spatial
coverage than the results of this study because of therestricted selection of the Match-pairs. Another important
point is the solar zenith angle observed on the trajectories.
Terao et al. [2002] mentioned that differences in the SZA on
the trajectories of satellite-Match and sonde-Match might
produce a significant difference in the derived ozone loss
amounts. Rex et al. [2003] also pointed out there may besome unresolved uncertainty in photodissociation efficiency
for large solar zenith angle. Therefore comparison of thetwo results is not straightforward.
[47] The lower ozone destruction than Match analysis
may be interpreted as underestimate of active chlorine in themodel. Some model studies reported lower ClO than MLS
ClO data [see Danilin et al., 2000, Plate 3; van den Broek etal., 2000, Plate 4]. To clarify this discrepancy, we needmore observation of chlorine species. Fortunately, ILAS
new version 6.0 of the algorithm succeeded to produce
ClONO2 recently. We are now pursuing a comparative study
with the Match analysis group with ClONO2 analysis, andresults will appear in the near future.

5. Concluding Remarks[48] The Chemical Species Mapping on Trajectories(CSMT) technique creates synoptic maps from asynoptic
data by using a photochemical box model and trajectory
analysis. Long and short lived species in the Arctic stratosphere were successfully mapped by CSMT. Initialization
was achieved using ILAS ozone, HNO3, and N2O. CSMTderived ozone data, which were compared to ozonesonde
data; the CSMT scheme preserved the reliability of ozone
data even after mapping. The CSMT-derived ozone, HNO3,
and ClO data were also compared with MLS version 5 data,
showing good consistency between the two data sets. Suchconsistency supports the validity of the CSMT technique.
[49] The CSMT technique yields useful information
about minor species where complicated chemical and/or
dynamical processes occur, as in the Arctic because thespatial distributions of long- and short-lived chemical species can be presented on a global scale. The CSMT method
can be categorized as a data-assimilation scheme. A characteristic of the technique is that chemical species are
mapped on grids only where the data are constrained byobservations. CSMT can be applied to any kind of data set.
It can produce global maps when combined with satellite
data with wide-coverage such as MLS. In this study,
however, the coverage of CSMT maps was restricted tonorth of 60N because of ILAS data limitations.
[50] CSMT showed significant chemical ozone loss in thelate winter and spring of 1997 in the Arctic vortex. The
maximum ozone loss rate was 34 ± 10 ppbv/day in late
February, and the integrated ozone loss in the polar vortex

HAYASHIDA AND KAGAWA: ARCTIC OZONE LOSS IN 1997

from 13 January to 31 March was 41%, averaged for the STS
case. Ozone decreased more for all periods when the warm
bias in the ECMWF temperatures was corrected. The maximum rate of mean ozone loss was 38 ± 13 ppbv/day in late
February. If NAT are assumed rather than STS, similar losses
occur: the maximum mean ozone loss was also 37 ± 11 ppbv/
day in late February. Results are fairly consistent withchemical ozone losses reported in other studies of the same
winter, which suggests that the model simulated the chemicalozone loss in this period reasonably well.
[51] Simultaneous observations by ILAS of ozone, HNO3,
H2O, and PSCs at relatively high resolution allow a detailed
discussion of ozone loss. Because the rate of ozone loss issensitive to solar zenith angle, ozone loss significantly
depends on latitude. Although ozone loss proceeded mainly
at lower latitudes until late February in the polar vortex, theregion of significant ozone loss shifted to higher latitudes inMarch. The chemical analysis in this study is still preliminary.
A more detailed analysis in a future study including otherminor species is possible.

Appendix A:
the Model

Chemical Species Included in[52] O(1D), O, O3H, OH, H2, HO2, H2O, H2O2N, NO,
NO2, NO3, N2O, HNO3, HO2NO2, N2O5Cl, ClO, Cl2,
OClO, ClOO, Cl2O2, HCl, HOCl, ClONO2Br, BrO, HBr,
HOBr, BrCl, BrONO2CH4, CHO, CH2O, CH3, CH2, CH3O,
CH3O2, CH3OOH, CO, CO2CH3Cl, CH3BrCCl4, CH3CCl3,
CFC11 (CCl3F), CFC12 (CCl2F2), CFC13 (CClF3)CFC113(CCl 2 FCClF 2 ), CFC114 (CClF 2 CClF 2 ), CFC115(CF3CClF2) HCFC22 (CHClF2), HCFC141B (CH3CCl2F),
HCFC142B (CH3CClF2) Halon1211 (CBrClF2), Halon1301(CBrF3)N2, O2 (O2 = 0.2095, N2 = 0.7905 MR)

Appendix B[53] Because radicals that destroy ozone are sensitive tosunlight, solar radiation should be computed precisely inmodels of the polar regions in winter and spring. Zsun, themaximum solar zenith angle determining sunlit duration, isdefined as:
Zsun ¼

pþ
2

rﬃﬃﬃﬃﬃ2z
ðin radiansÞ;aðB1Þwhere z is the altitude in km and a is Earth’s radius(6370 km). (When z = 20 km, Zsun is about 94.)[54] For solar zenith angles (q) from 0 to 75, air massfactors ( f ) that determine the attenuation of solar radiation
are defined by
F ¼ sec q:

ðB2Þ

From 75 to 90, the formula of the Garcia-Solomon model[Solomon et al., 1985] was used:
35
F ¼ pﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ :1224 cos2 q þ 1

ðB3Þ

[55] The Chapman function was used for 90 < q < Zsun
[Brasseur and Solomon, 1984]:
rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ!)
rﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ(
px
x sin q3;
F¼
sin q 1 þ erf  cot q
1þ
2
2
8x sin q

ðB4Þ

where erf(x) = p2ﬃﬃpheight (7 km).

ACH

3 - 19R x x2
dx, x = (a + z)/h, and h is the scale0e[56] Acknowledgments. We are grateful to Jean-François Lamarqueand Stacy Walters of the Atmospheric Chemistry Division, NCAR, forproviding the preprocessor. The authors thank Susan Solomon for making
her model available, and Sachiko Kawase, Nara Women’s University, forhelping to develop CSMT. Thanks also to Naoko Saitoh, Nara Women’s
University for providing PSC data by ILAS. This research used EORCTAM, a trajectory calculation tool developed by the Earth Observation
Research Center of NASDA. ILAS data were provided by ILAS/DHF at
NIES, and we would like to express our gratitude to Yasuhiro Sasano andall members of the ILAS science team.

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S. Hayashida, Faculty of Science, Nara Women’s University, Kita-uoya
Nishi-machi, Nara 630-8506, Japan. (sachiko@ics.nara-wu.ac.jp)
A. Kagawa, Fujitsu FIP Corporation, Time 24 Building, 2-45, Aomi,
Koto-ku, Tokyo 135-8686, Japan. (a.kagawa@fip.fujitsu.com)