Three-satellite comparison of polar mesospheric clouds: Evidence for long-term change

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 107, NO. D12, 4134, /2001JD000668, 2002 Three-satellite comparison of polar mesospheric clouds: Evidence for long-term change E. P. Shettle, 1 G. E. Thomas, 2 J. J. Olivero, 3 W. F. J. Evans, 4 D. J. Debrestian, 5 and L. Chardon 4 Received 26 March 2001; revised 23 August 2001; accepted 28 August 2001; published 19 June [1] Measurements of polar mesospheric clouds (PMCs) from three different satellite instruments are compared. These instruments are the Solar Mesospheric Explorer (SME), the Wind Imaging Interferometer (WINDII), and the Polar Ozone and Aerosol Measurement (POAM II). These measurements have been put on a common basis, correcting for differences in the wavelengths and measurement techniques used. This common basis is the probability distribution of the excess extinction ratio (EER) at a standard wavelength of 265 nm, where the EER is the ratio of the PMC extinction coefficient to the background molecular Rayleigh scattering coefficient. The results indicate that the POAM and WINDII measurements in the Southern Hemisphere had a higher probability of observing bright PMCs during the time period than SME did a decade earlier in Local time variations identified in WINDII data are interpreted in terms of a diurnal and semidiurnal component of average EER. These results are qualitatively similar to those found from lidar soundings of noctilucent cloud at sites in Norway and at the South Pole. Differences in interannual variability, local time of the measurements, assumed particle size distributions, and solar cycle effects are ruled out as possible explanations of the differences. INDEX TERMS: 1610 Global Change: Atmosphere (0315, 0325); 0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801); 0320 Atmospheric Composition and Structure: Cloud physics and chemistry; 0340 Atmospheric Composition and Structure: Middle atmosphere composition and chemistry; KEYWORDS: polar mesospheric clouds, noctilucent clouds, remote sensing, climate change, PMC, NLC 1. Introduction [2] Polar mesospheric clouds (PMC) are the spaceobserved manifestation of noctilucent clouds (NLC), observed from the ground since 1885 and suspected to be sensitive to global change in the middle atmosphere [Thomas, 1991, 1996]. Believed to be water-ice clouds that form near the very cold summertime mesopause region [Hervig et al., 2001], they have been observed from space by various instruments designed for other purposes, such as airglow or ozone studies; see Donahue et al. [1972], Thomas [1984], Evans et al. [1995], Debrestian et al. [1997a, 1997b], Carbary et al. [1999], and Thomas et al. [1999]. Gadsden [1998] described a time series of groundbased NLC observations ( ), suggesting that NLC are not only influenced by the 11-year solar cycle, 1 Naval Research Laboratory, Remote Sensing Division, Washington, D. C., USA. 2 Laboratory for Atmospheric and Space Physics, University of Colorado, Boulder, Colorado, USA. 3 Department of Physical Sciences, Embry-Riddle Aeronautical University, Daytona Beach, Florida, USA. 4 Department of Environmental Science, Trent University, Peterborough, Ontario, Canada. 5 Computational Physics, Inc., Fairfax, Virginia, USA. Copyright 2002 by the American Geophysical Union /02/2001JD but also exhibit a dramatic upward trend in occurrence frequency. Thomas et al. [1991] have proposed that there is a possible solar cycle dependence of bright PMC using solar backscattered ultraviolet nadir data. However, no longer-term trend has been yet identified in space-based data sets. This is undoubtedly due to the fact that up to now, no data has been available over a sufficient time period (however, Burton and Thomason [2000] have recently described a new version of Stratospheric Aerosol and Gas Experiment (SAGE II) retrievals which contain PMC signatures, covering the period 1985 to present). Debrestian et al. [1997a], in a preliminary comparison of Polar Ozone and Aerosol Measurement (POAM II) and Solar Mesospheric Explorer (SME) observations of PMCs, noted that POAM II appeared to see more bright PMCs than SME did at similar latitudes a decade earlier. [3] In this paper, we compare data from three spacecraft: the SME, the Wind Imaging Interferometer (WINDII), and the POAM II. We describe a method that allows intercomparisons of data on PMC from different satellite-borne instruments, taken in different eras. Measurements are referenced to a common standard (in this case, the neutral atmospheric density) and make allowance for differences in the measurement techniques, wavelengths, and spatial resolutions. The chosen metric of comparison is the statistical distribution of PMC extinction coefficients, referenced to the Rayleigh background, from the different satellites. ACL 2-1

2 ACL 2-2 SHETTLE ET AL.: THREE-SATELLITE COMPARISON OF PMCS Figure 1. Solar Lyman-a flux versus year. Shown are the times of measurements by SME (asterisks) and by POAM and WINDII (diamonds). Solar flux (a composite data set from Woods et al. [2000]) has been smoothed within a 90-day period to match the length of a PMC season. Significant differences between the 1980s and 1990s would provide a measure of the interdecadal changes in PMC occurrence, but only if the data apply to the same phase of the solar cycle. Fortunately, this is the case for the WINDII- POAM II era ( ) and the SME era ( ). This is illustrated in Figure 1, which shows the solar Lyman-a (La) flux over that time period [Woods et al., 2000]. La flux is expected to be the most relevant solar output for PMC forcing [Garcia, 1989; Thomas, 1995]. The La fluxes averaged over the PMC season for the two observing periods are within 10%. As described in section 2, we consider data from the Southern Hemisphere only. Excluding the 1981/1982 and 1982/1983 (summertime) SME periods, the fluxes are nearly identical for the three seasons 1983/1984, 1984/1985, and 1985/1986 and for the three seasons 1993/1994, 1994/1995, and 1995/1996; these are the comparison periods adopted. [4] This paper describes the three instruments and data analyses, the results for the statistical comparisons of PMC occurrence frequency, their local time dependence, and their implications for possible mesospheric long-term variability. 2. Description of the Experiments and Retrieval Algorithms 2.1. Discussion of POAM II PMC Measurements [5] The POAM II instrument was a nine-channel filter photometer [covering nm], which measured PMCs using the solar occultation technique. POAM was developed primarily to measure the vertical profiles of ozone and aerosols, and several other trace species, in the stratosphere and upper troposphere. However, it has proved sensitive enough to measure PMCs and has provided the first reported measurements of PMCs in atmospheric extinction [Debrestian et al., 1997a]. [6] POAM II made measurements from the French SPOT-3 satellite from October 1993 to November The timing of SPOT s sun-synchronous, inclination orbit meant that the POAM II measurements occurred at more poleward latitudes in the Southern Hemisphere than in the Northern Hemisphere. This resulted in nearly all of the POAM PMC observations being made in the Southern Hemisphere. The effective horizontal resolution is 200 km along the line of sight (LOS) and 30 km perpendicular to the LOS. POAM II observed 14 occultations per day in each hemisphere, which, during the PMC season, occurred between 2115 and 2200 LT in the Southern Hemisphere. Successive events on a given day occurred at slowly varying latitudes and were separated by 25 in longitude. During the Southern Hemisphere PMC season this latitude was primarily between 63 S and 68 S, and reached 73 S only at the end of the PMC season. A more detailed description of the POAM II instrument is given by Glaccum et al. [1996]. [7] The PMC events used for the present study are the same as those discussed by Debrestian et al. [1997a, 1997b], since their detection algorithm has been retained. The properties of the PMC were determined by fitting the measured optical thickness with a simple geometric cloud model. The basic model assumed that the PMC is of finite horizontal extent, with the extinction coefficient and vertical thickness assumed to be constant throughout the PMC. This model eliminated several simplifying assumptions made by Debrestian et al. [1997a] in deriving the cloud properties: First, the PMC are no longer required to be symmetric about the tangent point of the LOS (although the tangent point was still within the cloud); second, a correction is made for the 0.8 km-vertical field-of-view of the POAM instrument; and third, in calculating the slant path optical depth, the altitude of the cloud top and base is no longer neglected with respect to the Earth radius. The model was characterized by five parameters: the cloud top altitude; the cloud base altitude; the cloud extinction; and the apparent tangent altitudes of the near and far edge of the cloud as seen by the POAM II instrument along its LOS. Because of the modifications to the model utilized to fit the PMC properties, the retrieved properties of the individual PMCs differed from those derived by Debrestian et al. [1997a, 1997b]; however, the mean properties changed by <5 10%. In particular, the mean extinction coefficient (which is proportional to the excess extinction ratio (EER)), decreased by 3% for two of the PMC seasons and increased by 1% for the third PMC Observations from the SME Spacecraft [8] The basic data set is from the Ultraviolet Spectrometer Experiment [Rusch et al., 1984; Olivero and Thomas, 1986]. The spinning satellite (5 rpm) caused the rectangular instrument slit to scan the atmospheric limb at a spatial resolution of 3.5 km (vertical) 35 km. The prime spectral observing mode of the experiment was designed to measure ozone in the mesosphere and had channels at 265 and 296 nm with a 1.5-nm band pass. The UV radiance data at 265 nm has been averaged over six spins to obtain an average over roughly 5 of latitude for the near-polar orbit. The scattering coefficient k c at the limb point has been retrieved by a matrix inversion technique, described by Clancy and Rusch [1989]. The inversion corrects for the finite field-of-view, the tilt of the slit with respect to the horizontal, and the variable sensitivity along the length of the slit. We have improved the determination of the line-of-sight altitude by requiring that the data match those of a forward radiance model, including a simple homogeneous-layer model of a PMC (see Thomas and McKay [1985] for the relevant equations).

3 SHETTLE ET AL.: THREE-SATELLITE COMPARISON OF PMCS ACL 2-3 [9] A cloud is identified in the data by requiring that (1) the radiance exceed the midlatitude mean radiance plus 2 standard deviations in both channels, and (2) the cloud must occur at either 83.0 or 86.5 km. The inverted data product is the excess scattering ratio (ESR), evaluated at the limb point height of the PMC maximum [Thomas, 1995], ESR ¼ ½k c ðlþp c ðl; qþš= ½k R ðlþp R ðqþš: ð1þ [10] Here k c is the PMC scattering coefficient; P c (q) isthe PMC scattering phase function at the scattering angle q, which is the angle between the satellite viewing LOS to the PMC and from the PMC to the sun; k R is the Rayleigh scattering coefficient; P R (q) = (3/16p)(1 + cos 2 q)isthe Rayleigh angular scattering phase function for the background atmospheric Rayleigh scattering at the cloud height; and l is the wavelength, which is 265 nm for SME. The threshold for cloud detection in channel 1 at 83 km was ESR = 0.67, and the threshold was 1.03 in channel 2. The SME ESR quantity applies to a 3.5-km-thick region centered on a uniform altitude grid (79.5 km, 83 km, etc.). The spacecraft collected data from late 1981 to mid-1986, including five Southern Hemisphere PMC seasons. The near-polar sun-synchronous orbit allowed for measurements on both sides of the pole, causing the local time to be nearly constant at the POAM latitudes (1612 and 0348 LT). A three- to four-orbit daily sampling of the POAM latitude track region insured a uniform seasonal coverage of the entire PMC season, but with a bias toward certain longitudes. However, no systematic longitudinal dependence of PMC has been found either in the SME data [Thomas and Olivero, 1989] or in the WINDII data. [11] This version of the SME PMC database [Thomas et al., 2000] differs from older previous versions [e.g., Thomas, 1995] only for the dimmest clouds. For the bright clouds (ESR > 2) that are the focus of this paper, the results for the cloud identification are identical Discussion of WINDII PMC Measurements [12] The Wind Imaging Interferometer aboard the NASA UARS satellite was launched in September 1991 [Shepherd et al., 1993]. WINDII is a limb-viewing, imaging interferometer, which, in spectrally continuous light, behaves like a simple electronic camera, viewing the solar continuum scattered from the thin layer of particles edge-on in the limb. Since the UARS orbit has an inclination of 57, the orbit precesses westward at the rate of 20 min of local time per day. Hence, during a 4-week period the local time advances by 8 hours, permitting coverage of the diurnal variation from 0600 to 2000 LT, as will be demonstrated in Figure 5. Although WINDII observes numerous PMCs from 55 to 72, for purposes of comparison to the POAM latitudes, we have restricted the data set to those observations made between 62.5 and 67.5, which is the POAM latitude coverage during January when the WINDII PMC measurements were made. [13] The PMC observation mode consists of taking images of the limb through the WINDII Filter 1 in rapid succession as the field-of-view moves from 45 S through 72 S and back down to 45 S as the subsatellite point passes through its maximum latitude. Filter 1 is the background channel for green-line measurements and so contains no airglow features [Shepherd et al., 1993]. It is centered at nm and has a bandwidth of 1.6 nm full width at half maximum. The PMC-observing window included altitudes from 70 to 100 km and a width of 150 km, with CCD bins of 1 10 km. The PMCs appear as an almost horizontal line across a horizontally uniform background of Rayleigh scattered light that varies in intensity exponentially with altitude. The vertical distribution of the scattered light from the image in all 16 columns is used in the analysis. The PMC layer appears as a sharp spike on the otherwise monotonic Rayleigh background signal. We measure the increased brightness due to particle scattering above the Rayleigh atmospheric background. The Rayleigh scattered background was determined by fitting an exponential to the altitude profile of the observed scattering over a 10-kmscale distance. The exponential is interpolated across the altitude range where the clouds are dominant (5 km). This procedure works well independently of cloud brightness. After the Rayleigh background intensity has been calculated, the excess limb scattering ratio is determined from the ratio of the total limb brightness to the Rayleigh background brightness. This limb scattering radiance ratio is the normal product of the WINDII PMC analysis. [14] For purposes of the intercomparison, a new version of the algorithm was designed to construct a data set suitable for the simulation of the POAM and SME observational conditions. To match the 30-km horizontal field-ofview of the sun for POAM (and the 35-km slit width for SME), three of the 1 10 km CCD bins were combined. The result is a one-column profile from the center of each WINDII image. Then the data of the column profile from 100 km down to 88 km are used to fit an exponential atmosphere with a constant scale height of 5.6 km, corresponding to the temperature typical of the PMC-layer peak at 83 km. 3. Analysis [15] In comparing the PMC observations from the different satellite instruments, it is important to note that all three data sets refer to limb sounding, in which a PMC contribution can occur over several hundred kilometers along the LOS. This horizontal averaging is advantageous in that it somewhat reduces the effects of small-scale spatial structure, evident in lidar soundings [see, e.g., von Zahn and Bremer, 1999]. However, we must also take into account the differences in the nature of the measurements made by the instruments. While all three instruments observe the Earth s atmospheric limb, both SME and WINDII observe the PMCs in solar scattering, whereas POAM II measures the transmitted sunlight through the PMC. SME s primary measurements are at 265 nm, POAM s measurements at 448 nm are used in the present analysis, and WINDII s measurements are at 553 nm. There are many possible choices to put the different measurements on a common basis. Somewhat arbitrarily, we chose to convert the POAM II and WINDII measurements to the SME wavelength. In addition, because extinction is a more fundamental quantity than scattering, we corrected the SME and WINDII measurements to EER, the ratio of PMC extinction coefficient to the background Rayleigh extinction at the cloud altitude, EER = k c (265)/ k R (265). This conversion

4 ACL 2-4 SHETTLE ET AL.: THREE-SATELLITE COMPARISON OF PMCS from scattering to extinction, as well as the wavelength conversion, requires some assumptions regarding the PMC particle size distribution, which will be discussed below. [16] For SME (and also WINDII; see below) the excess scattering ratio at 265 nm, ESR, is defined by (1). This quantity is converted to an excess extinction ratio at 265 nm by effectively removing the effects of scattering angle. In principle, this requires the determination of P c (q)/p R (q) for each cloud. This is impossible for the normal observing mode (for special modes of operation of SME, this was achieved by Thomas and McKay [1985]). However, since there is a statistical relationship between the phase function P c (q) and cloud brightness [Thomas and McKay, 1985], we can estimate this quantity for every cloud, depending upon its brightness. This was achieved by comparing scattering ratios measured in the Northern and Southern Hemispheres [Thomas et al., 2000] for all latitudes. The scattering ratios were separated into 20 brightness bins in each hemisphere, defined by their percentile ranking, from the brightest (the top 5%, the top 10%, etc.) to the dimmest cloud. Since PMC properties in both north and south were found to differ only in the scattering angle at which they are viewed, the ratios of the scattering ratios in the respective percentile categories yield the ratios P c (q S )/P c (q N ). Here q S = 45 and q N = 128 are the average scattering angles in the south (S) and north (N), respectively. Since Mie scattering theory relates the phase function at different scattering angles to the mean particle radius, then given the lognormal size distribution, it is possible to infer the mean particle radius r 0, given the width parameter S (see (5)). Finally, with r 0 and S known, the Mie theory yields for a given value of ESR the final data product, the excess extinction ratio EER = k c (265 nm)/k R (265 nm). The ratio of ESR to EER lies between 0.5 and 1.0 in the south and between 1.0 and 2.0 in the north. For the weaker clouds along the POAM track, these factors were correspondingly closer to 1.0. The errors involved in assuming a specific size distribution are discussed in section 4. [17] Because of the undersampling of the cloud by the SME instrument, the excess scattering is underestimated compared with the 1-km resolution of POAM II and WINDII. This is because the limb radiance is measured with a 3.5-km grid interval, which is also the field-of-view (the direct effect of the 3.5-km field-of-view is removed in the inversion, mentioned above). On the basis of simulations of the effect of undersampling for clouds of different thicknesses, we find that this would lead to a 6% underestimate of the PMC extinction coefficients for cloud thickness of 1 3 km. The average of the cloud thicknesses for POAM II and WINDII was 2.55 km. It should be noted that this value is greater than that determined by lidar (1 2 km), which in some cases is <1 km. However, the effect of the long LOS over many cloud structures of variable height and thickness is to increase the effective cloud thickness. This 6% correction for undersampling has been included in the results discussed in section 4. [18] The use of the six-spin averages instead of the actual spin-by-spin distributions has only a small effect on the statistical distribution, because every cloud whose brightness is underestimated is at least partially compensated for by another cloud that is overestimated. See Figure 3 of Thomas [1984] for samples of the spin-by-spin data, illustrating the spatial patchiness along the orbit track. The sixspin averaging leads to the SME data being binned into 5 latitude bins, which correspond to 560 km. This is consistent with the lower limit of the km that Carbary et al. [2000] have derived for the typical horizontal dimensions of PMCs. [19] For WINDII the basic measurement is the limb scattered radiance at nm. To convert to the desired excess scattering ratio requires an inversion similar to the SME data analysis. However, a shortcut was necessary to avoid carrying out a completely new inversion of the large number of PMC measurements made by WINDII. Detailed inversions showed that it was sufficient to multiply the excess limb scattering ratio by the factor 2.0 to obtain ESR. In the inversion test an Abel transform inversion was used; the ratio of the peak inverted profile scattering ratio to the peak limb scattering ratio was used to calculate the factor. This factor was calculated from one full PMC season of WINDII limb inversions; a mean value of 2.0 with a standard deviation of 0.1 was determined. The inversion factor was also checked on two theoretical limb profiles. This single-factor approach is accurate enough for the analysis of a large data set, because the PMC-layer altitude is relatively constant and the layer thickness is not highly variable. [20] This value of ESR was then converted to EER, EER (265 nm) = k c (265 nm)/k R (265 nm), by EER ¼ ESRfk c ð265 nmþ= ½k c ðl w ÞP c ðl w ; qþšg k R ðl w ÞP R ðqþ =k R ð265nmþ : ð2þ Here l w denotes the WINDII wavelength of nm. In (2) the first factor on the right-hand side, ESR, is measured; the middle factor, in curly brackets, depends on the assumed PMC particle size distribution and the observed scattering angles; and the last factor, in brackets, can be calculated from knowledge of the wavelength and angular dependence of the Rayleigh scattering. [21] For POAM II the PMC extinction coefficient, k c (l p ), is directly derived from the measurements, where l p indicates the POAM primary PMC channel of nm. The EER for POAM II can be calculated as EER ¼ k c ðl p Þ k c ð265 nmþ=k c ðl p Þ =kr ð265 nmþ: ð3þ [22] In (3) only the middle factor on the right-hand side, in brackets, depends on the assumed PMC particle size distribution (as was the case for (2)).As discussed above, these conversions of the measurements to EER at 265 nm require assumptions regarding the size distribution of the PMC particles. However, the results are relatively insensitive over the range of typical sizes. To compare the various size distributions, we use the optical effective radius r 6 introduced by Jensen [1989], which is the sixth root of the sixth moment of the size distribution. It is related to the parameters for a monomodal lognormal size distribution by r 6 ¼ r 0 exp ð18 ln 2 1=6; sþ ð4þ

5 SHETTLE ET AL.: THREE-SATELLITE COMPARISON OF PMCS ACL 2-5 Fraction of Observations > EER POAM II ( ) WINDII ( ) SME ( ) Excess Extinction Ratio = (PMC Ext.)/(Rayleigh) Figure 2. Fraction of all observations that contain a PMC with an excess extinction ratio, EER, greater than the indicated value. EER is the ratio of extinction by the PMC to molecular Rayleigh extinction at a wavelength of 265 nm. where the lognormal size distribution is given by n h io nðrþ ¼ N 0 = r ln sð2pþ 1=2 exp ln 2 ðr=r 0 Þ= 2ln 2 s ; ð5þ where N 0 is the total number density of particles, r 0 is the mode radius, and s is the width parameter. The advantage of the optically effective radius r 6 is that it reduces the apparent sensitivity of the PMC scattering to the width s of the size distribution, compared with using the mode radius r 0. Rusch et al. [1991] reported r 6 = 43 ± 16 nm; Debrestian et al. s [1997a] results lead to bounds on r 6 of nm; the results of von Cossart et al. [1999] are equivalent to r 6 = 73 ± 30 nm; and the results of Alpers et al. [2000] are equivalent to values of 39 and 45 nm for r 6. These are also consistent with Jensen s [1989] microphysical models A, B, and C, which had r 6 values of 30, 43, and 71 nm, respectively. For most of the analysis below we will use Jensen s [1989] model B, with r 6 = 43 nm, or we will use r 0 = 30 nm with a width parameter of 1.413, as used by Rusch et al. [1991] and by Debrestian et al. [1997a] and close to the s = 1.43 derived by von Cossart et al. [1999]. We will also examine the sensitivity of the conclusions to this restriction. 4. Results and Discussion [23] To compare the different satellite instruments, we will use the cumulative distribution of the EER for the PMCs measured by each instrument, g(eer), which is the fraction of all observations during the PMC season that are greater than the EER. We have taken the PMC season to be from 30 days before summer solstice to 60 days after summer solstice [Olivero and Thomas, 1986]. However, because of operational constraints the WINDII PMC measurements were limited to a 26- to 35-day period, which corresponded roughly to the month of January. Because of its orbit, POAM II had the most limited latitude coverage during the PMC season, from 62.7 Sto73 S, with nearly all the observations occurring between 62.7 S and 67.5 S, only getting closer to the South Pole in the last few weeks of the season. For this reason, we restricted the SME and WINDII observations to those made near the POAM latitudes (we call this the POAM track). This is important since the results of Olivero and Thomas [1986] and Thomas and Olivero [1989] showed that there was a strong latitudinal dependence in the probability of PMC occurrence. [24] The use of the cumulative probability distributions, g(eer), allows us to focus on the probability of occurrence of the brightest clouds, that is, those with the largest values of EER. This minimizes any differences in the sensitivity of the different instruments. However, to further reduce the effects of detection sensitivity, the probability of occurrences for the least sensitive instrument, which was POAM II, were adjusted to correct for the probability of detection of the weaker PMCs. This correction factor was based on Monte Carlo simulations on the ability of POAM II to detect modeled PMCs of various geometric thicknesses and extinction coefficients, including the appropriate measurement noise. The effect of this correction is to increase the probability of POAM II detecting a PMC by 80% for the dimmest PMCs where it is reliably estimated (EER 5), by 40% for EER 10, and going to 0% for EER > 27. [25] In Figure 2 we show the distribution of g(eer) plotted for all three satellite experiments, each for the 3-year period considered. There is fair agreement between the two curves at the top, i.e., for WINDII and POAM, which have contemporary data. The bottom curve belongs to the SME data set taken years earlier. Figure 2 certainly appears to show that the PMC occurrence frequencies are different in the two time periods. The latter period has more bright clouds along the POAM track. [26] To investigate whether interannual variability could account for the differences between the two time periods, we look at the data for the individual years. Figure 3 shows the cumulative probabilities for each experiment for each of the three PMC seasons. There is certainly significant interannual variability shown here, especially for WINDII, which had limited seasonal coverage. Even on a year-toyear basis, however, it appears that there are generally fewer

6 ACL 2-6 SHETTLE ET AL.: THREE-SATELLITE COMPARISON OF PMCS Figure 3. Interannual variability for PMCs measured by the SME (green), POAM II (red), and WINDII (blue) satellite instruments. See color version of this figure at back of this issue. PMCs in the 1980s than in the 1990s. Many of the differences between POAM and WINDII can be accounted for by the WINDII observations being limited to January rather than covering the full PMC season. For example, the POAM II measurements shown in Figure 4 indicate that December 1994 had many more PMCs than January 1995, whereas December 1995 had significantly fewer PMCs than January These month-to-month variations could account for much of the observed year-to-year differences between POAM and WINDII. Qualitatively similar seasonal variations have also been observed in the SME results when restricted to the POAM latitude track and to the brighter PMCs [EER > 4]. A possible explanation of these variations is that because the clouds are observed near the extreme latitudes at which the upper mesosphere and mesopause are cold enough to sustain the development of PMCs, the timing of the occurrence of the clouds is more sensitive to year-to-year differences in the large-scale dynamics. [27] Another issue that is important to this intercomparison is the local time differences of the measurements. Tides affect the temperature, the winds, and thus the distribution of mesospheric ice particles [Jensen et al., 1989]. The atmospheric tidal variations are not adequately observed or modeled at present. Luckily, the precession of the UARS Figure 4. Probability of POAM II observing a PMC as a function of date for the three different seasons. See color version of this figure at back of this issue. Figure 5. Diurnal variation of EER from WINDII (asterisks). Error bars indicate 1 standard deviation for the WINDII measurement in each 2-hour time bin. Solid line shows the fit to the WINDII data by (6). Corresponding SME and POAM II values are indicated by crosses and diamonds, respectively. orbit causes the WINDII data set to encompass a wide range of (dayside) local times. Thus we shall use the relative variations of WINDII PMC observations with local time to establish an approximate correction factor. Figure 5 shows averaged values of the WINDII EER versus local time of observation. Also shown are the averages and local times of the SME and the POAM observations. [28] We have fit the WINDII EER variations as a function of the local time, T, as the sum of diurnal and semidiurnal components: f ðtþ ¼A 0 þ A 1 sin½2pðt þ A 2 Þ=24ŠþA 3 sin½2pðt þ A 4 Þ=12Š: ð6þ [29] This fitted function is also shown in Figure 5. The values of the fitting parameters were calculated to be A 0 = 10.67, A 1 = 1.073, A 2 = 0.822, A 3 = 1.001, and A 4 = Von Zahn et al. [1998] and Chu et al. [2001] have noted similar diurnal plus semidiurnal variations in the lidar backscatter of PMC for sites in Norway and at the South Pole, respectively, although shifted by several hours. Their variations also show a greater dynamic range, to be expected since their data apply to specific locations on the globe, whereas our data are longitudinal averages. To derive the correction factors, the ratio of the average value of all the WINDII measurements to the value of this function (equation (6)) at the times of the POAM II and SME observations was used. For the POAM II data the correction factor for averaged daytime conditions was For SME the correction factors were and for the morning and afternoon measurements, respectively. If we apply these corrections to the data sets, we can construct local time corrected cumulative occurrence probability distributions similar to Figure 2. We show this in Figure 6, in which the curves have shifted a bit. However, it is still the case that the 1990s measurements from WINDII and POAM II are in general agreement with each other and are substantially higher than the distributions for SME (1980s data). In fact, with this correction for the local time of the measurements, the difference between the 1980s data from SME and the 1990s data from POAM and WINDII has increased.

7 SHETTLE ET AL.: THREE-SATELLITE COMPARISON OF PMCS ACL 2-7 Fraction of Observations > EER Corrected POAM II WINDII Corrected SME Excess Extinction Ratio = (PMC Ext.)/(Rayleigh) Figure 6. Frequency of observing a PMC with greater than a given value of EER, corrected for diurnal variations. EER is the ratio of extinction by the PMC to molecular Rayleigh extinction at a wavelength of 265 nm. Fraction of Observations > EER Fraction of Observations > EER (a) Corrected POAM II WINDII Corrected SME Model A Excess Extinction Ratio = (PMC Ext.)/(Rayleigh) (b) Corrected POAM II WINDII Corrected SME Model C Excess Extinction Ratio = (PMC Ext.)/(Rayleigh) Figure 7. Similar to Figure 6 but utilizing two different assumed particle size distributions, (a) model A and (b) model C, to convert the different satellite measurements to a common basis. See text for details.

8 ACL 2-8 SHETTLE ET AL.: THREE-SATELLITE COMPARISON OF PMCS [30] Finally, we examine the sensitivity of our results to the assumed particle size model, Jensen s [1989] model B. To illustrate the two extreme possibilities, the g(eer) distribution for Jensen s lognormal models A (r 0 = 22 nm, s = 1.35) and models C (r 0 = 45 nm, s = 1.48) are shown in Figure 7. It should be noted that model B is consistent with the SME north-south ratios, with the SME leading-trailing ratios [Thomas and McKay, 1985], and with the ratios of the images in the two WINDII look directions (oriented 90 to one another). The model A phase function is nearly equivalent to Rayleigh scattering, and model C pertains to larger particles, which are appropriate to the higher latitudes where the PMC are considerably brighter. As discussed above, Jensen s [1989] PMC particle size models cover the range of possible particle sizes derived from various measurements. Comparing Figure 6 with Figure 7, the tendency remains for the g(eer) values for SME to be lower than those for POAM and WINDII a decade later, independent of the choice of size distribution used to convert the various measurements onto a common basis. 5. Summary and Conclusions [31] We have described a statistical method to compare measurements of PMC taken at different eras with different instrumentation and local times. The basis of comparison is the cumulative distribution of PMC brightness (more accurately, excess extinction ratios) at the SME wavelength of 265 nm for averaged dayside local times. We find that the POAM II and WINDII data, taken in 3 years during the mid-1990s, indicate many more bright clouds than occurred in a similar 3-year period of the SME era of the mid-1980s. Although there are significant interannual and local time variations, these are not serious enough to prevent us from drawing the conclusion that an important interdecadal change occurred in PMC activity. We have also eliminated likely uncertainties in the assumed particle size distribution as an explanation of the differences between the measurements during the two decades and have minimized the possible effects of solar cycle variability. While our analysis focused on the latitude region of 60 S 70 S, it can be argued that this region near the outer edge of where PMCs are normally detected is particularly sensitive to changes. PMCs are thought to be forced by temperature, water vapor, the concentration of cloud nuclei, and dynamics [Thomas, 1996]. The question of whether these forcings have changed from the 1980s to the 1990s is beyond the scope of this paper. However, our results of brighter, more frequent clouds in recent years are consistent with those of Gadsden [1998] for NLC in the Northern Hemisphere. Thus, although our study provides no clues as to the cause, it adds additional validity to the notion that long-period changes in mesospheric clouds are worldwide, impacting both summertime polar regions. [32] Although we have concentrated on the interdecadal variability in this paper, it was necessary to obtain the local time variability in order to make a valid comparison of measurements made at differing local times. Although these results are important in their own right, we have not dwelt on the possible implications for tidal variations. However, we have identified both diurnal and semidiurnal components in the longitudinally averaged WINDII PMC data that suggest that tidal modulation of the forcing variables is important in the Southern Hemisphere summer mesopause region (latitudes 60 S 70 S). [33] Acknowledgments. Funding for the POAM instrument was provided by ONR. E. P. S. s and D. J. D. s research was supported by NRL. NASA s Offices of Space Science and Earth Science also supported portions of E. P. S. s research. The Aeronomy Program of the National Science Foundation sponsored G. E. T. s research. W. Evans was supported by an NSERC collaborative grant for WINDII. WINDII operations and the instrument were supported by the Canadian Space Agency. We would like to thank Z. Chen of LASP for his assistance with some of the calculations. References Alpers, M., M. Gerding, J. Höffner, and U. von Zahn, NLC particle properties from a five-color lidar observation at 54 N, J. Geophys. Res., 105, 12,235 12,240, Burton, S. P., L. W. Thomason, Polar mesospheric clouds in SAGE II version 6.0 data, paper presented at Quadrennial Ozone Symposium, Int. Ozone Comm., Sapparo, Japan, 3 8 July Carbary, J.-F., G. J. Romick, D. Morrison, L. J. Paxton, and C. I. Meng, Altitudes of polar mesospheric clouds observed by a middle ultraviolet imager, J. Geophys. Res., 104, 10,089 10,100, Carbary, J.-F., D. Morrison, and G. J. Romick, Transpolar structure of polar mesospheric clouds, J. Geophys. Res., 105, 24,763 24,769, Chu, X., C. S. Gardner, and G. Pappen, Lidar observations of polar mesospheric clouds at the South Pole: Diurnal variations, Geophys. Res. Lett., 26, , Clancy, R. T., and D. W. Rusch, Climatology and trends of mesospheric (58 90 km) temperatures based upon SME limb scattering profiles, J. Geophys. Res., 94, , Debrestian, D. J., J. D. Lumpe, E. P. Shettle, R. M. Bevilacqua, J. J. Olivero, J. S. Hornstein, W. Glaccum, D. W. Rusch, and M. D. Fromm, Preliminary analysis of Southern Hemisphere POAM II observations of polar mesospheric clouds, J. Geophys. Res., 102, , 1997a. Debrestian, D., J. Lumpe, R. M. Bevilacqua, E. P. Shettle, J. S. Hornstein, and J. J. Olivero, POAM II observations of polar mesospheric clouds in the Southern Hemisphere, Adv. Space Res., 19, , 1997b. Donahue, T. M., B. Guenther, and J. E. Blamont, Noctilucent clouds in daytime: Circumpolar particulate layers near the summer mesopause, J. Atmos. Sci., 30, , Evans, W. F. J., L. R. Laframboise, K. R. Sine, R. H. Wiens, and G. G. Shepherd, Observation of polar mesospheric clouds in summer 1993 by the WINDII instrument on UARS, Geophys. Res. Lett., 22, , Gadsden, M., The north-west Europe data on noctilucent clouds: A survey, J. Atmos. Sol. Terr. Phys., 60, , Garcia, R. R., Dynamics, radiation, and photochemistry in the mesosphere: Implications for the formation of noctilucent clouds, J. Geophys. Res., 94, 14,605 14,615, Glaccum, W., et al., Polar Ozone and Aerosol Measurement (POAM II) Instrument, J. Geophys. Res., 101, 14,479 14,487, Hervig, M., R. E. Thompson, M. McHugh, L. L. Gordley, J. M. Russell III, and M. E. Summers, First confirmation that water ice is the primary component of polar mesospheric clouds, Geophys. Res. Lett., 28, , Jensen, E., A numerical model of polar mesospheric cloud formation and evolution, Ph.D. thesis, Univ. of Colo., Boulder, Jensen, E., G. E. Thomas, and O. B. Toon, On the diurnal variation of noctilucent clouds, J. Geophys. Res., 94, 14,693 14,702, Olivero, J. J., and G. E. Thomas, Climatology of polar mesospheric clouds, J. Atmos. Sci., 43, , Rusch, D. W., G. H. Mount, C. A. Barth, R. J. Thomas, and M. T. Callan, Solar Mesospheric Explorer ultraviolet spectrometer measurements of ozone in the 1.0- to 0.1-mbar region, J. Geophys. Res., 89, 11,677 11,687, Rusch, D. W., G. E. Thomas, and E. J. Jensen, Particle size distributions in polar mesospheric clouds derived from Solar Mesospheric Explorer measurements, J. Geophys. Res., 96, 12,933 12,939, Shepherd, G. G., et al., WINDII, the Wind Imaging Interferometer on the Upper Atmosphere Research Satellite, J. Geophys. Res., 98, 10,725 10,750, Thomas, G. E., Solar Mesosphere Explorer measurements of polar mesospheric clouds (noctilucent clouds), J. Atmos. Terr. Phys., 46, , Thomas, G. E., Mesospheric clouds and the physics of the mesopause region, Rev. Geophys., 29, , 1991.

9 SHETTLE ET AL.: THREE-SATELLITE COMPARISON OF PMCS ACL 2-9 Thomas, G. E., Climatology of polar mesospheric clouds: Interannual variability and implications for long-term trends, in The Upper Mesosphere and Lower Thermosphere: A Review of Experiment and Theory, Geophys. Monogr. Ser., vol. 87, edited by R. M. Johnson and T. E. Killeen, pp , AGU, Washington, D. C., Thomas, G. E., Global change in the mesosphere-lower thermosphere region: Has it already arrived?, J. Atmos. Terr. Phys., 58, , Thomas, G. E., and C. P. McKay, On the mean particle size and water content of polar mesospheric clouds, Planet. Space Sci., 33, , Thomas, G. E., and J. J. Olivero, Climatology of polar mesospheric clouds, 2, Further analysis of Solar Mesospheric Explorer data, J. Geophys. Res., 94, 14,673 14,702, Thomas, G. E., R. D. McPeters, and E. J. Jensen, Satellite observations of polar mesospheric clouds by the solar backscattered ultraviolet spectral radiometer: Evidence for a solar cycle dependence, J. Geophys. Res., 96, , Thomas, G. E., S. M. Bailey, and C. A. Barth, Observations of polar mesospheric clouds by the Student Nitric Oxide Explorer (abstract), Eos Trans. AGU, 80(46), Fall Meet. Suppl., F765, Thomas, G. E., J. J. Olivero, and Z. Chen, Optical and physical properties of polar mesospheric clouds inferred from Solar Mesospheric Explorer limb sounding data (abstract), Eos Trans. AGU, 81(48), Fall Meet Suppl., SA11A-01, von Cossart, G., J. Fiedler, and U. von Zahn, Size distribution of NLC particles as determined from three-color observations of NLC by ground-based lidar, Geophys. Res. Lett., 26, , von Zahn, U., and J. Bremer, Simultaneous and common-volume observations of noctilucent clouds and polar-mesosphere summer echoes, Geophys. Res. Lett., 26, , von Zahn, U., G. von Cossart, and J. Fiedler, Tidal variations of noctilucent clouds measured at 69 N latitude by ground-based lidar, Geophys. Res. Lett., 25, , Woods, T. N., W. K. Tobiska, G. J. Rottman, and J. R. Worden, Improved solar Lyman-a irradiance modeling from 1947 through 1999 based on UARS observations, J. Geophys. Res., 105, 27,195 27,215, L. Chardon and W. F. J. Evans, Department of Environmental Science, Trent University, Peterborough, Ontario K9J7B8, Canada. (wevans@ trentu.ca) D. J. Debrestian, Computational Physics, Inc., 8001 Braddock Rd., Suite 210, Springfield, VA 22151, USA. J. J. Olivero, Department of Physical Sciences, Embry-Riddle Aeronautical University, Daytona Beach, FL 32114, USA. (OliveroJ@cts.db. erau.edu) E. P. Shettle, Naval Research Laboratory, Code 7227, Remote Sensing Division, 4555 Overlook Ave., SW, Washington, D. C , USA. (shettle@nrl.navy.mil) G. E. Thomas, Laboratory for Atmospheric and Space Physics, University of Colorado, Campus Box 392, Boulder, CO , USA. (Gary.Thomas@lasp.colorado.edu)

10 SHETTLE ET AL.: THREE-SATELLITE COMPARISON OF PMCS Figure 3. Interannual variability for PMCs measured by the SME (green), POAM II (red), and WINDII (blue) satellite instruments. Figure 4. Probability of POAM II observing a PMC as a function of date for the three different seasons. ACL 2-6