JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117, D05311, doi: /2011jd016789, 2012

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1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 117,, doi: /2011jd016789, 2012 Ozone loss rates in the Arctic winter stratosphere during derived from POAM II/III and ILAS observations: Implications for relationships among ozone loss, PSC occurrence, and temperature Yukio Terao, 1 Takafumi Sugita, 1 and Yasuhiro Sasano 1 Received 26 August 2011; revised 6 January 2012; accepted 6 January 2012; published 10 March [1] Quantitative chemical ozone loss rates at the 475 K isentropic surface inside the Arctic polar vortex are evaluated for six winters (January through March) using a satellite-based Match technique. Satellite observational data are taken from the Polar Ozone and Aerosol Measurement (POAM) II for , the Improved Limb Atmospheric Spectrometer (ILAS) for 1997, and the POAM III for The largest ozone loss rates were observed in the end of January 1995 (50 20 ppbv d 1 ), February 1996 ( ppbv d 1 ), February 1997 (40 8 ppbv d 1 ), January 2000 (60 30 ppbv d 1 ), and early March 2000 (40 10 ppbv d 1 ). The probability of polar stratospheric cloud (PSC) existence is estimated using aerosol extinction coefficient data from POAM II/III and ILAS. Ozone loss and the PSC probability are strongly correlated and an absolute increase of 10% in the PSC probability is found to amplify the chemical ozone loss rate during Arctic winter by approximately 25 6 ppbv per day or ppbv per sunlit hour. Relationships between average Arctic winter ozone loss rates and various PSC- and temperature-related indices are investigated, including the area of polar vortex that is colder than the threshold temperature for PSC existence (A PSC ), the PSC formation potential (PFP), and the potential for activation of chlorine (PACl). Of these three, PACl provides the best proxy representation of interannual variability in Arctic ozone loss at the 475 K level. Large ozone loss occurred primarily for air masses that experienced low temperatures between 187 K and 195 K within the previous 10 days and the ozone loss rates clearly increase with decreasing the minimum temperature. The particularly large ozone losses of 9 3 ppbv per sunlit hour in February 1996 and January 2000 were associated with low minimum temperatures of K, simultaneously with high PSC probabilities. Citation: Terao, Y., T. Sugita, and Y. Sasano (2012), Ozone loss rates in the Arctic winter stratosphere during derived from POAM II/III and ILAS observations: Implications for relationships among ozone loss, PSC occurrence, and temperature, J. Geophys. Res., 117,, doi: /2011jd Introduction [2] Stratospheric ozone in the polar region during winter and spring varies substantially from year to year. This interannual variability (IAV) is due to both chemical processes (chlorine- and bromine-catalyzed ozone destruction) and dynamical processes (transport by the residual circulation and mixing across the edge of the polar vortex). The monthly averages of total column ozone during early spring (March in the Arctic and October in the Antarctic) observed from satellite instruments, such as the Total Ozone Mapping Spectrometer (TOMS) or the Ozone Monitoring Instrument (OMI), provide useful measures of the IAV in stratospheric 1 Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Japan. Copyright 2012 by the American Geophysical Union /12/2011JD ozone depletion; however, these observational representations of IAV cannot differentiate between dynamical and chemical variations. In the Arctic, the IAV in planetary wave activity affects both transport into the polar vortex and chemical ozone loss, thereby contributing significantly to the IAV in stratospheric ozone [Tegtmeier et al., 2008a]. This strong dependence of Arctic ozone on dynamics makes it challenging to diagnose the chemical loss rate of ozone from observations that also include the effects of transport and mixing [World Meteorological Organization (WMO), 2011]. [3] Several methods have been proposed to quantitatively evaluate the chemical loss amounts of ozone. These methods include: the ozone-tracer correlation method (first used by Proffitt et al. [1990]), the vortex-average method [Manney et al., 1994; Braathen et al., 1994], the Match method [von der Gathen et al., 1995], the passive subtraction method [Lefèvre et al., 1998], and the Lagrangian transport calculation method [Manney et al., 1997]. The details and 1of19

2 references for each of these methods have been summarized by WMO [2003, 2007]. Arctic ozone losses derived using different methods or data generally agree quantitatively. Examples of this agreement have been published for the winters of 1994/1995, 1995/1996, and 1996/1997 [Harris et al., 2002], 1999/2000 [Newman et al., 2002], and 2004/ 2005 [WMO, 2007]. These comparative studies have only discussed accumulated ozone loss either on monthly time scales or for the whole winter spring, and have not examined local ozone loss rates. Of the aforementioned methods, the Match method is the only one that can derive local ozone loss rates over short periods. [4] The Match technique [von der Gathen et al., 1995; Rex et al., 1998] uses pairs of ozonesonde profiles ( matches ) that have been obtained at separate locations but can be traced to the same air mass using Lagrangian trajectory analysis. This approach allows dynamical effects on ozone changes to be neglected. Only chemical changes remain between the first and the second observations of each matching pair, meaning that the chemical ozone loss rate and amount can be estimated quantitatively. The Match method has been used to analyze the temporal evolution of ozone loss rates in the polar vortex for the Arctic winters of 1991/ 1992 [von der Gathen et al., 1995; Rex et al., 1998], 1992/ 1993 and 1993/1994 [WMO, 2003], 1994/1995 [Rex et al., 1999], 1995/1996 [Rex et al., 1997], 1996/1997 [Schulz et al., 2000], 1997/1998 and 1998/1999 [Schulz et al., 2001], 1999/2000 [Rex et al., 2002], 2000/2001 [WMO, 2003], 2002/2003 [Streibel et al., 2006], and 2004/2005 [Rex et al., 2006; Harris et al., 2010]. Other studies have examined uncertainties in the Match analysis technique [Lehmann et al., 2005; Morris et al., 2005; Grooß et al., 2008]. [5] The Match analysis method requires intensive measurements of ozone with a high vertical resolution throughout the polar stratosphere. These requirements are met by solar occultation sensors onboard sun-synchronous polarorbiting satellites, such as the Improved Limb Atmospheric Spectrometer (ILAS), the Polar Ozone and Aerosol Measurement (POAM) II, and the POAM III. Among satellite-borne instruments, these solar occultation sensors are capable of the highest vertical resolution measurements because they use the bright sun as a light source. With the sensor in sun-synchronous polar orbit, approximately 14 ozone profiles per day are obtained over high-latitude regions in both the Northern Hemisphere (NH) and Southern Hemisphere. The data quality of the satellite-borne sensor measurements is spatially and temporally homogeneous, which represents an advantage over ozonesonde measurements. [6] We have previously applied the Match technique to ILAS ozone profile data to successfully analyze quantitative chemical ozone loss rates and amounts in the Arctic polar vortex for the winter and spring of 1997 [Sasano et al., 2000; Terao et al., 2002]. To compensate for the weaknesses of the satellite sensor data (i.e., a lower vertical resolution and a larger volume of air sampled than ozonesonde data), the analysis method has been refined by adding multiple trajectories with very strict criteria [Terao et al., 2002]. This modification allows the method to more accurately identify double-sounded air masses. Terao [2003] extended the satellite-match analysis to POAM II and POAM III data, and presented preliminary estimates of ozone loss rates in the Arctic polar stratosphere for six winters between 1994 and The satellite-match technique has also been applied to POAM III measurements in the Antarctic [Hoppel et al., 2005]. In this paper, we present and discuss comprehensive evaluations of Arctic chemical ozone loss rates derived from POAM/ILAS-Match analysis and analyze these results in the context of recent findings regarding polar stratospheric cloud (PSC) and temperaturerelated indices. [7] The relationship between chemical ozone loss and temperature should be quantitatively established in regard to possible future changes in stratospheric temperatures [e.g., Stolarski et al., 2010]. PSCs may form at temperatures below approximately 195 K in the polar stratosphere. PSCs are variously composed of nitric acid trihydrate (NAT; known as Type Ia PSC), supercooled ternary (H 2 SO 4 /HNO 3 / H 2 O) solutions (STS; known as Type Ib PSC), and ice particles (known as Type II PSC) [e.g., Peter, 1997; Lowe and MacKenzie, 2008]. The saturation temperature for NAT (or threshold temperature for NAT existence, T NAT ) can be calculated from stratospheric concentrations of water vapor (H 2 O) and nitric acid (HNO 3 )[Hanson and Mauersberger, 1988]. The threshold temperature for STS, which is several Kelvins lower than T NAT, can be similarly parameterized [Tabazadeh et al., 1994; Carslaw et al., 1995]. The geographical area or volume of vortex air for which the temperature is lower than T NAT (A PSC or V PSC ) is conventionally used as a measure of chlorine activation due to PSCs, and hence ozone losses in the polar region [Rex et al., 2004, 2006; Tilmes et al., 2004]. [8] Liquid binary (H 2 SO 4 /H 2 O) solutions could also play an important role for chlorine activation in the winter polar vortices through heterogeneous reactions, even in the presence of moderate amounts of volcanic aerosol that followed the eruption of Mt. Pinatubo in 1991 [e.g., Kawa et al., 1997; Engel et al., 2000; Massie et al., 2000]. The threshold temperature for activation of chlorine (T ACl ) has therefore also been suggested as a useful proxy for the chemical loss of ozone [Drdla, 2005; Drdla and Müller, 2010]. T ACl is defined as the temperature at which active chlorine (ClO x = ClO + 2 ClOOCl) increases by an amount equivalent to 10% of the total inorganic chlorine (Cl y ) over a period of 1 day. Chlorine activation of 10% is equivalent in the current atmosphere to about 0.35 ppbv of ClO [Drdla and Müller, 2010]. T ACl has been used in several studies, particularly with regard to geoengineering schemes for enhancing stratospheric sulfate aerosols and possible changes in stratospheric water vapor concentrations [Tilmes et al., 2007, 2008; Feck et al., 2008]. [9] Previous studies have shown that V PSC correlates well with observed column Arctic ozone losses accumulated over the entire winter [Rex et al., 2004; Tilmes et al., 2004]. The observed correlation has been reproduced by a threedimensional (3-D) chemical transport model [WMO, 2007]. The actual occurrence of PSCs does not necessarily correspond to V PSC, however. Observations by a satellite-borne aerosol lidar, the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), show that the vertically integrated area of observed PSCs is generally smaller than V PSC in both the Arctic and Antarctic [Pitts et al., 2007, 2009; WMO, 2011]. These results suggest that estimates of PSC 2of19

3 Figure 1. (a) Latitudes of POAM II, ILAS, and POAM III measurements in the Northern Hemisphere (NH) from 1993 to 2000; (b) latitudes of POAM II/III and ILAS measurements in the NH for the winter period (January March); and (c) locations of ILAS and POAM III measurement points in the NH on 1 January and 1 March (ILAS in 1997 and POAM III in 2000). occurrence derived from T NAT -based V PSC are likely to be biased high. Tilmes et al. [2006, 2007, 2008] proposed two different indices, the T NAT -based PSC formation potential (PFP) and the T ACl -based potential for activation of chlorine (PACl), and reported that column Arctic ozone losses correlate better with PACl than PFP. Some chemistry climate models (CCM) qualitatively reproduced correlations between ozone loss and PACl but the observed and simulated correlations quantitatively differed [SPARC CCMVal, 2010]. Comprehensive observational studies are thus required to better quantify the empirical relationships among ozone loss rates, observed PSCs, and temperature. [10] This study analyzes observational evidence of relationships among ozone loss rates, PSC occurrence, and temperature during the Arctic winter, also considering PSCand temperature-related indices (A PSC, PFP, and PACl). Previous studies have shown empirical correlations between the IAVs of ozone loss and these indices [e.g., Rex et al., 2004; Tilmes et al., 2004, 2006, 2008; Müller et al., 2008; Harris et al., 2010]; however, these studies have not shown the IAV of PSC occurrence directly. The ILAS and POAM II/III provide concurrent observations of both ozone mixing ratio and aerosol extinction coefficient (AEC). The occurrence of PSCs can be estimated by statistical analysis of the AEC data. The POAM/ILAS Match analysis provides unique and important information on relationships among observed ozone loss rates and PSC existence. [11] We derive chemical ozone loss rates in the Arctic for the six winters of 1993/1994, 1994/1995, 1995/1996, 1996/ 1997, 1998/1999, and 1999/2000 on the 475 K isentropic surface (approximately 19 km in altitude), which serves as a representative level for the lower stratosphere. Previous studies have reported large ozone losses within this analysis period, particularly during the winters of 1995/1996, 1996/ 1997, and 1999/2000: the accumulated ozone loss around the 475 K level was ppmv (parts per million volume) between 29 January and 3 March 1996; ppmv between 30 January and 21 March 1997 [Harris et al., 2002]; and 1.7 ppmv (20%) between 20 January and 12 March 2000 [Newman et al., 2002]. The ozone loss was small during the winters of 1997/1998 and 1998/1999 [Schulz et al., 2001]. The satellite measurements from the Microwave Limb Sounder (MLS) [Manney et al., 2003] and Halogen Occultation Experiment (HALOE) [Tilmes et al., 2004] also showed the large ozone losses in the Arctic winters of 1995/1996 and 1999/2000. [12] The data and analysis method are described in section 2. The time series of ozone loss rates is presented in section 3.1. The time series of PSC occurrence and relationships between ozone loss and PSC occurrence, A PSC, PFP, and PACl are shown in section 3.2. The minimum temperatures experienced during the preceding 10 days by the corresponding air parcels are examined in section 3.3. The IAV in T ACl during , when moderate amounts of sulfate aerosols had existed, is discussed in section 4, and the results are compared with those of ozonesonde-match analyses. The conclusions of the study are summarized in section Data and Methods [13] Figure 1a shows the zonal and temporal coverage of POAM II, ILAS, and POAM III measurements in the NH. Each sensor covers the latitude region of 55 N 71 N. The measurement periods extend from October 1993 to November 1996 for POAM II, November 1996 to June 1997 for ILAS, and April 1998 to December 2005 for POAM III. The latitudes of POAM II/III and ILAS measurements during the winter spring period of January, February, and March (herein JFM) range from 64 N at the beginning of 3of19

4 January to 68 N (POAM II/III) or 70 N (ILAS) at the beginning of March (Figure 1b). The difference in latitudes between POAM II/III and ILAS measurements is small, with a maximum difference of 2 degrees in March. The POAM II/III and ILAS measurements were made over a limited range of latitudes but covered all longitudinal regions (Figure 1c) POAM II/III Data [14] POAM II and POAM III were developed by the United States Naval Research Laboratory. POAM II was launched onboard the Satellite Pour l Observation de la Terre (SPOT) 3 on 26 September 1993 [Glaccum et al., 1996]. POAM III, which succeeded POAM II, was launched onboard SPOT 4 on 23 March 1998 into the same orbit as SPOT 3 [Lucke et al., 1999]. POAM II and POAM III instruments were solar occultation sensors with a 9 channel photometer. The photometer on POAM II covered wavelengths between 352 nm and 1060 nm with a spectral resolution of nm and the photometer on POAM III covered wavelengths between 354 nm and 1018 nm with a spectral resolution of nm. The instantaneous fieldof-view (IFOV) of POAM II/III in the vertical direction was approximately 0.8 km. POAM II provided vertical profiles of ozone, nitrogen dioxide, and AECs at six wavelengths (352.3, 441.6, 601.4, 781.0, 921.0, and 1060 nm), and POAM III provided vertical profiles of ozone, nitrogen dioxide, water vapor, and AECs at six wavelengths (353.4, 442.3, 603.4, 779.4, 922.4, and 1018 nm). The SPOT 3 and SPOT 4 satellites were launched into a sun-synchronous subrecurrent orbit at 833 km altitude with an inclination of 98.7 degrees and a period of 101 min. The local mean Sun time at the descending node was 10:30. [15] The present study uses vertical profiles of ozone mixing ratio and AEC at 1060 nm for POAM II and at 1018 nm for POAM III. The data were retrieved using the POAM II V6 [Lumpe et al., 1997] and POAM III V3 [Lumpe et al., 2002] retrieval algorithms. Error estimates for ozone retrievals using POAM II V6 are the same as for those using the V5 retrieval algorithm. The POAM II V5 ozone retrievals were validated by comparisons with ozonesonde measurements [Deniel et al., 1997] and with satellite measurements from MLS, HALOE, and Stratospheric Aerosol and Gas Experiment (SAGE) II instruments [Rusch et al., 1997]. Random and systematic errors in the POAM II V5 ozone retrievals are both approximately 5% between 10 km and 50 km altitude. A comparative study of ozone profiles measured by seven satellite instruments, including the POAM II V6 data, reported that these observations of ozone generally agree to within 0.25 ppmv in the lower stratosphere [Manney et al., 2001]. Another comprehensive comparison of multiple satellite-borne sensors revealed a negative bias of up to 0.4 ppmv in POAM II V6 ozone in the lower stratosphere [Danilin et al., 2002]. [16] The POAM III V3 ozone retrievals were validated by comparison with observations from balloon-borne, aircraftborne, and ground-based instruments made during the SAGE III Ozone Loss and Validation Experiment and Third European Stratospheric Experiment on Ozone (SOLVE/ THESEO) 2000 campaign [Lumpe et al., 2003]. The POAM III ozone data agree with the SOLVE/THESEO data to within 7 10% with no detectable bias between 14 km and 30 km altitude. Randall et al. [2003] compared POAM III V3 ozone measurements with both ozonesonde measurements and satellite observations from the HALOE and SAGE II instruments, and showed that agreement among these data sets is generally within 5% from 13 km to 60 km in the NH. The latest POAM III retrieval is V4; however, the V4 ozone data have changed very little from the V3 data, and the results are not sensitive to the use of V3 rather than V4. [17] The AEC data from POAM II V6 at 1060 nm and POAM III V3 at 1018 nm have been compared with SAGE II data at 1020 nm. For POAM II, differences relative to SAGE II are within 10% between 12 km and 27 km and random errors are approximately 10% below 25 km [Randall et al., 2000]. For POAM III, differences relative to SAGE II are within 30% between 10 km and 22 km and random errors are less than 20% below 22 km [Randall et al., 2001]. During the lifetime of POAM III, there has been some instrument degradation that affects the retrieval of AEC (POAM III V4 description); however, the effect of this degradation is small through before increasing significantly in early [18] Figure 2 shows daily variations in the number of POAM II/III and ILAS measurements (open columns) and in the number of measurements inside the polar vortex at the 475 K isentropic surface (green) in the NH from 1 January to 31 March in 1994, 1995, 1996, 1997, 1999, and In this study, we focus on ozone loss rates and PSC occurrence inside the polar vortex. The vortex edge and the vortex boundary region were defined for each day using a method similar to that of Nash et al. [1996] in which potential vorticity (PV) gradient and wind speed were used. We followed the Nash et al. method, except that the location of the vortex edge and boundary region was constrained by the derivative of the product of PV gradient by wind speed to reduce multiple peaks of PV gradient [Ninomiya and Nakane, 1998]. [19] Throughout the JFM period, POAM II/III and ILAS made observations both inside and outside the polar vortex during all analyzed years. In 1994, the number of POAM II measurements was smaller by half when compared with measurements in the following years both because data collection was compromised by an alternating day on/day off schedule and because of data loss during early mission operation. The number of measurements inside the vortex is smaller in 1995 than in the other winters because the vortex area was small during February and March (shown later). In 2000, some episodes of missing POAM III data occur during early January and mid-february. The numbers of total measurements and measurements inside the vortex during the JFM period are summarized in Table 1. We did not analyze POAM III data after 2000 because the number of POAM III measurements was reduced by half after mid- March in The total numbers of POAM III measurements in the NH during JFM are 644 in 2001, 600 in 2002, 1072 in 2003, 650 in 2004, and 790 in These numbers are similar to POAM II in 1994, which produced very few double-sounded air parcels (Table 1). Although we have only used data from 1999 and 2000, the POAM III measurements are crucial to this study because they provide insight into episodes of no ozone loss in 1999 and huge ozone loss in of19

5 Figure 2. Number of total measurements (open columns), number of measurements inside the polar vortex (green columns), number of measurements with temperature below 195 K (blue columns), and number of detected polar stratospheric cloud (PSC) events (red columns) for each day at the 475 K isentropic surface in the NH according to POAM II ( ), ILAS (1997), and POAM III ( ) observations from 1 January to 31 March ILAS Data [20] ILAS was developed by the Ministry of the Environment, Japan (formerly Environment Agency of Japan), and launched onboard the Advanced Earth Observing Satellite (ADEOS) on 17 August ILAS was a solar occultation sensor that consisted of a light-collecting telescope, a sun-tracking device, a sun-edge sensor, electrical circles, and two grating spectrometers [Sasano et al., 1999]. ILAS provided vertical profiles of gaseous species (ozone, nitric acid, nitrogen dioxide, methane, nitrous oxide, and water vapor) using a 44 channel infrared spectrometer that covered wavelengths between 6.21 mm and mm with a spectral resolution of 0.13 mm, and AEC, temperature, and pressure using a 1024 channel visible spectrometer covering Table 1. Total Number of Measurements, Number of Measurements Made Inside the Vortex, and Number of Matching Pairs Inside the Vortex on the 475 K Isentropic Surface Observed by POAM II/III and ILAS for 1 January Through 31 March of the Specified Year Sensor Year Measurements Inside the Vortex Match Pair POAM II POAM II POAM II ILAS POAM III POAM III of19

6 wavelengths between 753 nm and 784 nm. The IFOV at tangent height was 1.6 km in the vertical direction and 13 km in the horizontal direction. ADEOS was launched into a sun-synchronous subrecurrent orbit at approximately 800 km altitude with an inclination of 98.6 degrees and a period of 101 min. The local mean Sun time at the descending node was 10:40. [21] We use vertical profiles of ozone mixing ratio and AEC at 780 nm obtained by the ILAS V5.20 retrieval algorithm [Yokota et al., 2002]. The average estimated rootsum-square total uncertainties in the ozone retrievals are 14% at 15 km, 9% at 20 km, and 5% at 25 km in the NH between November 1996 and June The measurement repeatability is estimated to be 7% at 15 km and 3% at 20 km. The gas mixing ratios derived from ILAS may be biased for measurements that were collocated in space and time with PSCs [Yokota et al., 2002]; we have therefore eliminated ozone data that may have been measured in the presence of PSCs. The number of PSC-reduced ILAS profiles is shown in Table 1. [22] The V5.20 ILAS ozone data have been validated against ozone data obtained by ozonesondes, by balloonborne and aircraft-borne instruments, by ground-based instruments, and by the satellite sensors HALOE, SAGE II, and POAM II [Sugita et al., 2002]. The agreement between ILAS ozone data and these other data is generally within 10%. Focusing on the wintertime NH, comparison with the ozonesonde data yields relative differences of 14% at km and 8% at km during February, and of 7% at km and 5% at km during March. The V5.20 ILAS AEC data have been compared with SAGE II data that were converted to ILAS wavelength (780 nm) by log linearly interpolated using AEC at 525 nm and 1020 nm [Saitoh et al., 2002]. Differences between ILAS and SAGE II retrievals of AEC are generally within 10 20% for AEC values that exceed km Satellite-Match Analysis [23] The analysis method that is used for calculating ozone loss rates in this study is nearly identical to that used by Terao et al. [2002], with the exception of the diabatic descent rates and coordinates for the trajectory analysis. Here we briefly summarize the method and describe changes; additional details of the satellite-match method are provided by Terao et al. [2002]. [24] POAM II/III and ILAS data are provided as functions of geometrical altitude with 1 km intervals. The vertical coordinate is transformed to a potential temperature coordinate using United Kingdom Meteorological Office (UKMO) assimilation data [Swinbank and O Neill, 1994], and the ozone mixing ratios and AECs are interpolated onto the 475 K potential temperature surface for analysis. The UKMO assimilation data are also used for meteorological analysis and trajectory calculations. The UKMO data are provided daily at 12:00 universal time (UT) on a (latitude longitude) grid on the 22 Upper Atmosphere Research Satellite (UARS) standard pressure levels. [25] Ten day forward trajectories are used to search for air parcels that were sounded twice by POAM II/III or ILAS at different locations and at different times (double-sounded parcels, or match pairs). The trajectory analysis is conducted using an isentropic Lagrangian trajectory model supplied by the UKMO [Terao et al., 2002.] We calculate a set of forward trajectories starting from/around the first measurement point, and a set of backward trajectories starting from/around the matching second measurement point. Several criteria are applied to evaluate trajectory dispersions and check the accuracy of each match pair using a cluster of both forward and backward trajectories. Terao et al. [2002] developed the coordinates for these trajectory clusters to account for the volume of the air mass sampled by the ILAS measurement. The volume of an air mass sampled by a POAM II/III measurement is different from that sampled by an ILAS measurement, however: POAM II/III has a larger IFOV in the horizontal direction than ILAS. We therefore introduce alternative coordinates for the trajectory clusters. Four trajectories are initialized 100 km to the north, south, east, and west of the measurement point at the 475 K level, and two trajectories are initialized at the measurement point location but at 15 K in potential temperature. In total, 14 trajectories (one forward trajectory from the first measurement point, a cluster of six forward trajectories surrounding the first measurement point, one backward trajectory from the second measurement point, and a cluster of six backward trajectories surrounding the second measurement point) are calculated for each matching pair. The distance criteria for selecting double-sounded air parcels are unchanged from those used by Terao et al. [2002]. The ILAS data are reanalyzed using the trajectory coordinates defined for this study, rather than those defined by Terao et al. [2002]. [26] The calculation of the change in ozone mixing ratio between two observations of a double-sounded air parcel accounts for changes in potential temperature due to diabatic effects. The ozone mixing ratio of the second measurement is modified to account for any potential temperature changes along the trajectory. We use diabatic cooling rates calculated using SLIMCAT 3-D chemical transport model [Chipperfield, 1999]. [27] A statistical treatment is applied to calculate rates of ozone change for each day. We identify a subset of doublesounded air parcels that are gathered within 7 days prior to and following the target day. Assuming that ozone changes are linearly proportional to the sunlit time along the trajectory, we calculate a proportional coefficient (ozone change against sunlit time) from each subset using the least squares method. The rate of ozone change in ppbv (parts per billion volume) per sunlit hour (ppbv sunlit h 1 ) can be converted to a rate of ozone change per day (ppbv d 1 ) by multiplying by the average sunlit time (in hours) for each day. We also calculate ozone change rates using a regrouped data set related to minimum temperatures along 10 day backward trajectories from double-sounded air parcels in section 3.3: A subset of double-sounded air parcels obtained in the minimum temperature range of 1 K (from K to K) is used for regression analysis for each month and for each year Detection of PSC [28] Observations of AEC made by satellite-borne sensors have previously been used to detect PSCs. For example, Poole and Pitts [1994] proposed an approach to identifying PSCs and identified a trend in the frequency of PSCs using 6of19

7 Figure 3. Time series of ozone mixing ratios (ppmv) on the 475 K isentropic surface in the Arctic observed by POAM II ( ), ILAS (1997), and POAM III ( ). Solid black circles indicate measurements inside the polar vortex. the Stratospheric Aerosol Measurement II (SAM II) instrument. In their approach, they first calculated the median value and median deviation, referred to as the background value and deviation, respectively, of AEC for every 10 day period at each altitude level inside the vortex. Only observations for which the temperature exceeded 200 K were used to calculate the background value and deviation. They then defined the threshold value used to identify PSCs as three times the median deviation above the median value. Values of AEC larger than this threshold were regarded as PSCs provided that the temperature was lower than 200 K. [29] Fromm et al. [1997] applied a similar method to POAM II AEC at 1060 nm to develop a 3 year Antarctic PSC climatology. This POAM II PSC detection algorithm was later revised by Fromm et al. [1999] and adapted to POAM III AEC by Bevilacqua et al. [2002]. The threshold value for these studies was determined as 2.7 standard deviations above the mean. Hayashida et al. [2000] and Saitoh et al. [2002] employed similar PSC detection methods for ILAS AEC at 780 nm, and determined the threshold value as five standard deviations above the mean. [30] The present study detects PSCs using POAM II/III and ILAS AEC data at the 475 K isentropic level. The background reference extinction value and its deviation are calculated in the same way as in these previous studies (i.e., the mean and standard deviation are calculated for each 10 d period from all AEC data inside the vortex for which the temperature exceeds 200 K). The threshold value is defined as three standard deviations above the mean, which is 7of19

8 Figure 4. Locations of double-sounded air parcels identified inside the vortex as a function of equivalent latitude (sine scale) on the 475 K isentropic surface. POAM II/III and ILAS measurement points are marked by circles, and a matching pair of observations is connected by a solid line. Solid and dashed curves indicate the polar vortex edge and boundary region, respectively. between the values defined by Fromm et al. [1999] for POAM and Hayashida et al. [2000] for ILAS. 3. Results 3.1. Ozone Loss Rates [31] Figure 3 shows ozone mixing ratios observed by POAM II (1994, 1995, and 1996), ILAS (1997), and POAM III (1999 and 2000) at the 475 K isentropic surface in the Arctic region for the JFM period. Measurements made inside the polar vortex are marked by solid black circles. Figure 3 roughly illustrates the evolution of ozone mixing ratios inside the vortex during JFM. During JFM 1994, ozone mixing ratio decreased slightly from 3 ppmv (mid-january) to 2.5 ppmv (end of March). Two ozone depletion episodes occurred during JFM 1995: in early February and in mid- March. During 1996, ozone mixing ratios decreased from 2.5 to 3.0 ppmv in January to ppmv in March. JFM 1997 is characterized by continuous ozone depletion from the end of January, when ozone mixing ratios inside the vortex are greater than 3 ppmv, to the end of March, when the minimum ozone mixing ratios reach 1.5 ppmv. The ozone mixing ratios inside the vortex vary substantially in March 1997 (observed values ranging from 1.5 ppmv to 3.4 ppmv in the end of March), indicating that the magnitude of chemical ozone loss is strongly dependent on location relative to the vortex [Terao et al., 2002]. Ozone concentrations increased throughout the winter of 1999; this is the natural evolution of polar ozone during winters when chemical ozone depletion is weak. Large ozone depletions occurred during JFM 2000, with ozone mixing ratios inside 8of19

9 the vortex decreasing from 3.5 ppmv at the beginning of January to near 1 ppmv in mid-march. [32] Figure 4 shows the locations of double-sounded air parcels obtained inside the vortex for each Arctic winter as a function of equivalent latitude on the 475 K level. Equivalent latitude is based on the area enclosed by contours of PV. The double-sounded air parcels are distributed in space throughout the polar vortex, from the center of the vortex to the vortex edge, especially in 1996, 1997, and We therefore define the ozone change rates derived from the match pairs within the vortex as vortex-averaged values. [33] Figure 4 also shows the winter spring evolutions of the vortex edge and the vortex boundary region for each year. A warming event was observed in February 1995, and the vortex area was relatively small (north of Nin equivalent latitude) during February and March of that year. In 1996, the stratospheric final warming began in early March, and the area of the vortex started to diminish from the beginning of March. In 1997, the polar vortex formation occurred relatively late in the season, and the vortex edge was not clear before 9 January. The analysis is therefore only performed for observations made after 10 January Once formed, the polar vortex in 1997 was both very strong and symmetric around the North Pole, and it persisted until late spring [Coy et al., 1997]. During JFM 1999 the polar vortex was very weak and determination of the vortex edge using PV is only successful for February. We define the vortex edge as 65 N equivalent latitude from 1 January to 30 January. This is a typical location for the vortex edge in the other winters, and double-sounded air parcels at equivalent latitudes higher than 65 N are considered to be inside the vortex for the analysis. The breakup of the vortex occurred in early March in 1999, so the analysis for that year was terminated on 1 March. The vortex breakup in 2000 also occurred relatively early in the season, in late March, so the analysis was terminated on 20 March. [34] Table 1 shows the number of double-sounded air parcels observed inside the vortex by ILAS and POAM II/III during JFM. In the satellite-match analysis, matching pairs are identified by chance within the existing data set inside the vortex. This differs from the intentional creation of match pairs in real time during an ozonesonde-match campaign, in which ozonesonde launches are coordinated with trajectory forecasts. The number of matching pairs in this study therefore varies interannually, and depends on the shape and location of the polar vortex relative to the POAM II/III and ILAS measurement locations. The number of double-sounded air parcels is very small for 1994 because very few observations were made inside the vortex. The statistical significance of detected ozone change rates in 1994 was therefore lower than those in other years. The number of double-sounded air parcels identified for 1995 is larger than for 1994 but still smaller than for the following years. We obtain more than a hundred matching pairs during JFM in 1996, 1997, 1999, and The largest number of matching pairs is identified in ILAS observations from [35] Ozone change rates (in ppbv d 1 ) derived from the POAM/ILAS-Match analysis are shown in Figure 5. In 1994, the local maximum ozone loss rates were ppbv d 1 in mid-january and ppbv d 1 in late February; however, the statistical significance of the ozone change rates derived for 1994 is small. Less than ten double-sounded air parcels were included in the calculation of each ozone change rate, resulting very large error bars. The number of double-sounded air parcels is also relatively small for 1995, and ozone change rates in February and March were calculated from less than ten matching events. A relatively larger number of double-sounded air parcels were identified during January The local maximum ozone loss rate was ppbv d 1 in the end of January (50 20 ppbv d 1 for a running average). A second local maximum ozone loss rate of ppbv d 1 occurred during mid-march, but the uncertainty in this value is quite large. In 1996, which was a very cold winter in the Arctic stratosphere [Manney et al., 1996], the maximum ozone loss rates occurred at the beginning of January (50 20 ppbv d 1 ), and prolonged significant ozone loss rates ( ppbv d 1 ) occurred throughout February. Ozone losses were moderate from early March The statistical errors for ozone change rates derived for 1996 are smaller than those derived for 1994 or [36] The winter of 1997 is characterized by continuous ozone loss at rates of ppbv d 1 from the end of January through the end March. The maximum ozone loss rate is 46 8 ppbv d 1 between late February and early March. A small peak in ozone loss occurred in late January. The ILAS measurements for 1997 provided the largest number of double-sounded air parcels of the six analyzed winters. ILAS therefore produced ozone change rates with high accuracy and the small statistical uncertainty. Terao et al. [2002] presented an in-depth analysis and discussion of ozone loss in [37] The largest ozone loss of the six analyzed winters occurred in 2000, following minimal ozone loss in The local maximum ozone loss rates during 2000 were approximately ppbv d 1 (60 30 ppbv d 1 for a running average) during mid-january and approximately ppbv d 1 (40 10 ppbv d 1 for a running average) during late February and early March PSCs and Ozone Loss Rates [38] The number of PSC events per day (red column) and the number of measurements with temperatures below 195 K (blue column) at the 475 K isentropic surface are shown in Figure 2. POAM II, ILAS, and POAM III observed PSCs in every winter but 1999, when no PSCs were detected. Most of the PSCs were observed when temperature was below 195 K. PSCs were observed for two periods in 1994: in mid- January, and at the end of February and beginning of March. In 1995, PSCs were observed from January to early February, along with an isolated PSC event in mid-march (although temperature for this event exceeded 195 K). In 1996, PSCs were observed continuously throughout January and February into early March. Intermittent PSC events were observed in 1997, both in mid-january and from mid-february through mid-march. A large number of PSCs were observed in 2000, particularly in January. [39] The total number of measurements and the number of measurements inside the polar vortex vary from day to day and from year to year. Thus, the count of PSC events does not serve as an equivalent index for PSC activity in different portions of the JFM season, nor can it be used to compare and contrast PSC activity during different years. Instead, we 9of19

10 Figure 5. Time series of ozone change rates (ppbv per day) (squares, scale on the left-hand ordinate) and polar stratospheric cloud (PSC) probability (%) (red columns, scale on the right-hand ordinate in red) on the 475 K isentropic surface. Ozone change rates are plotted every 7 days with an error bar of 1 standard error. Black squares indicate the ozone change rate with statistical significance higher than 95% by the t test. Figures along the top of each panel show the numbers of double-sounded air parcels used to calculate the associated ozone change rates. The solid curve indicates the 15 day running mean of daily ozone change rates. The blue curve indicates the geographical areas where temperatures have been below the threshold temperature for PSC formation (A PSC ) at 475 K (scale on the right-hand ordinate in blue, adopted from WMO [2003]). introduce a probability of PSC occurrence, which is defined as the ratio (%) of the number of PSCs detected to the number of measurements inside the vortex [Fromm et al., 1999]. [40] The PSC probability for each day is shown along with the ozone change rates in Figure 5 (red columns). The highest PSC probabilities (50%) were observed in January and mid-february of 1996, in January of 1997, and in January of The PSC probability was 100% on 11 February 1996; however, this value is questionable because only two observations were made inside the vortex on that day. PSC probabilities were consistently smaller during February and March than during January. [41] The patterns of PSC probabilities well correspond to the geographical areas where temperatures have been below the threshold temperature for PSC formation (A PSC ) at 475 K (blue curve in Figure 5; adopted from WMO [2003, Figures 3 30]). The coefficient of determination (r 2 ) between PSC probability and A PSC is 0.76 for JFM, 0.71 for January, 0.84 for February, and 0.34 for March. This indicates that the PSC probability is a reasonable diagnostic for the whole vortex, even though the POAM II/III and ILAS measurements are made in narrow latitude regions. 10 of 19

11 Figure 6. Relationships between polar stratospheric cloud (PSC) probabilities and averaged ozone change rates (a d) per day and (e h) per sunlit hour on the 475 K isentropic surface during (Figures 6a and 6e) January March (JFM), (Figures 6b and 6f) January, (Figures 6c and 6g) February, and (Figures 6d and 6h) March. The numbers next to each data point indicate the year (YY). The solid line is the reduced major axis (RMA) regression fit. The coefficient of determination (r 2 ) and regression coefficient (b) are noted in each figure. Error bars encompass 1 standard deviation. However, the small sample of the vortex might have missed several PSC events, resulting in discontinuity of observed PSC probabilities. [42] The patterns of high PSC probabilities (and A PSC ) are very similar to those of high ozone loss rates, although they are not always contemporaneous. The highest ozone loss rates either occur simultaneously with or slightly lag spikes in the PSC probability (i.e., in January and March in 1995, in the beginning of January and February in 1996, between mid-february and early March in 1997, and in January and between mid-february and mid-march in 2000). Chlorine activation occurs because of low temperatures and/or large surface area densities of aerosols and PSCs. Ozone losses subsequently occur in the presence of active chlorine during sunlit times. It is therefore not surprising that large ozone losses do not always immediately take place when PSC probabilities are high. We therefore examine the relationship between ozone losses and PSC probabilities from the perspective of longer time scales (monthly and JFM). [43] Figure 6 shows relationships between the PSC probability and the averaged ozone change rate (in ppbv d 1 and in ppbv sunlit h 1 ) for the JFM seasons and the individual months of January, February, and March. We consider the vortex time period for calculations hereafter: day 1 90 for 1994, 1995, and 1996; day for 1997; day 1 60 for 1999; day1 80 for The PSC probability shown here was calculated directly for the corresponding period in each year (for individual months or for JFM). Errors in the PSC probability are estimated based on the square of the number of samples [Fromm et al., 1999]. The average ozone change rate is calculated for each year as the mean of daily ozone change rates for the corresponding period, and errors in the average ozone change rate represent one standard deviation of the distribution of daily change rates. [44] The ozone change rate and the PSC probability are negatively correlated, except for March for which no correlation is found. The r 2 value between PSC probability and ozone change rate per day is 0.56 for JFM, 0.66 for January, 0.85 for February, and 0.04 for March (Figures 6a 6d). The slope of the reduced major axis (RMA) regression equation is , , and (ppbv d 1 % 1 ) for JFM, January, and February, respectively. If March 1996 is removed, however, an obvious correlation can be identified for March 1995, 1997, and 2000 (r 2 is 0.97 and the regression coefficient is ppbv d 1 % 1 ). The ozone change rates during March are discussed in further detail in section 4. The correlation is highest and the slope steepest for February (and March if 1996 is removed) than for January. The result for JFM suggests that an absolute increase of 10% in the probability of PSC occurrence during Arctic winter typically leads to an increase of 25 6 ppbv d 1 in the ozone loss rate. [45] The seasonal increase in slope of the correlation from January to February/March is very small for ozone change rate per sunlit hour (Figures 6e 6h). The regression coefficient is , , and (ppbv sunlit h 1 % 1 ) for JFM, January, and February, respectively. This indicates that the relationship between the 11 of 19

12 Figure 7. Relationships between averaged ozone change rates (ppbv per sunlit hour) on the 475 K isentropic surface during January March (JFM) and (a) the JFM-averaged geographical areas where temperatures have been below the threshold temperature for polar stratospheric cloud (PSC) formation (A PSC ) at 475 K (adopted from WMO [2003]), (b) the PSC formation potential (PFP) at 475 K, and (c) the potential for activation of chlorine (PACl) at 475 K [Tilmes et al., 2008; S. Tilmes, personal communications, 2011]. The numbers next to each data point indicate the year (YY). The solid line is the reduced major axis (RMA) regression fit. The coefficient of determination (r 2 ) is noted in each figure. Error bars encompass 1 standard deviation. PSC probability and ozone loss rate per sunlit hour is almost constant during the winter. More sunlight in the late winter leads to the increase in slope of the relationship in February and March for ozone loss rate per day. The r 2 between PSC probability and ozone change rate per sunlit hour is similar to that for ozone change rate per day: 0.62 for JFM, 0.64 for January, 0.88 for February, and 0.07 for March (0.91 if 1996 removed). Against sunlight, the result for JFM suggests that an absolute increase of 10% in the PSC probability leads to an increase of ppbv sunlit h 1 in the ozone loss rate. [46] The ozone loss rates are highly sensitive to the PSC probability during January and February, suggesting that IAV in PSC probability is a good measure for IAV in ozone loss rates during January and February. For JFM and January, the ozone loss rate in 1996 was approximately half that in 2000, although the PSC probability in 1996 was roughly the same as (January) or larger than (JFM) that in The derived ozone change rates per sunlit hour for 1996 and 2000 differ in the same way. The differences in January ozone loss between 1996 and 2000 substantially reduce the calculated correlation between ozone loss rate and PSC probability. These differences are discussed further in section 4. [47] Figure 7 shows the relationship between averaged ozone change rates (in ppbv sunlit h 1 ) during JFM and various PSC- and temperature-related indices: JFM-averaged A PSC, and PSC formation potential (PFP) [Tilmes et al., 2006] and potential for activation of chlorine (PACl) [Tilmes et al., 2007]. A PSC shown here is the area of the vortex air for which the temperature is lower than T NAT at the 475 K level, whereas V PSC is the volume of the vortex area at temperatures below T NAT between 400 K and 550 K [Rex et al., 2004]. PFP is calculated as V PSC divided by the volume of the vortex for each day that the vortex can be identified between 400 K and 550 K. The daily values of PFP are integrated for each winter and then divided by the length of the period considered (from mid-december to the end of March). PFP extends the concept of V PSC by taking into account the differences in vortex conditions from year to year (i.e., IAV in the duration and volume of the vortex). PACl is calculated similarly to PFP, but uses the volume of the vortex area at temperatures below T ACl (V ACl ) rather than V PSC. PACl is scaled to match the trend of effective equivalent stratospheric chlorine (EESC). Here we use PFP and PACl values at the 475 K level (S. Tilmes, personal communications, 2011) for consistently comparing with ozone loss rates observed at 475 K. The meteorological data and vortex definition are different between our analysis and Tilmes (personal communications, 2011). [48] Correlations with ozone change rate are found for all three indices, with r 2 values of 0.90 for A PSC, 0.89 for PFP, and 0.94 for PACl. The correlation with ozone change rate is highest for PACl rather than A PSC or PFP, indicating that PACl is a better indicator of ozone losses at 475 K during JFM. The correlation between ozone change rate and A PSC / PFP is substantially degraded by the large value of A PSC / PFP in 1996 and the small value of A PSC /PFP in Our results agree with the results of Tilmes et al. [2008], who showed that PACl provided the best correlation with column ozone losses between 400 K and 550 K. [49] Ther 2 values between ozone loss rates and temperaturebased quantities (A PSC, PFP, and PACl) are higher than that for the PSC probability (Figures 6a and 6e). The largest difference between the PSC probability and three temperaturebased quantities is observed in 1996: The PSC probabilities during JFM in 1996 and 2000 are similar, but A PSC, PFP, and PACl are larger in 2000 than in The PSC probability derived from POAM and ILAS AEC data is a direct measure of PSC occurrence, however, the samplings are limited for the latitudes of 64 N 70 N thus the PSC probability might be a less good measure as a representation for the whole vortex. On the other hand, A PSC, PFP, and PACl are a good measure for the whole vortex but these temperature-based quantities are likely to be biased high on estimates of PSC occurrence [Pitts et al., 2007, 2009; WMO, 2011]. The advantage of this 12 of 19