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2 Icarus 217 (2012) Contents lists available at ScienceDirect Icarus journal homepage: Quantification of middle and lower cloud variability and mesoscale dynamics from Venus Express/VIRTIS observations at 1.74 lm Kevin McGouldrick a,, Thomas W. Momary b, Kevin H. Baines c, David H. Grinspoon d a Laboratory for Atmospheric and Space Physics, University of Colorado, 1234 Innovation Dr., Boulder, CO 80303, United States b Jet Propulsion Laboratory, M/S , California Institute of Technology, 4800 Oak Grove Dr., Pasadena, CA 91109, United States c Department of Space Science and Engineering, University of Wisconsin, 1225 W. Dayton St., Madison, WI 53706, United States d Department of Space Sciences, Denver Museum of Nature & Science, 2001 Colorado Blvd., Denver, CO 80205, United States article info abstract Article history: Available online 23 July 2011 Keywords: Venus, Atmosphere Atmospheres, Dynamics Atmospheres, Evolution Infrared observations Meteorology We present an analysis of VIRTIS-M-IR observations of 1.74 lm emission from the nightside of Venus. The 1.74 lm window in the near infrared spectrum of Venus is an ideal proxy for investigating the evolution of middle and lower cloud deck opacity of Venus because it exhibits good signal to noise due to its brightness, good contrast between bright and dark regions, and few additional sources of extinction beside the clouds themselves. We have analyzed the data from the first 407 orbits (equivalent to 407 Earth days) of the Venus Express mission to determine the magnitude of variability in the 1.74 lm radiance. We have also performed an analysis of the evolution of individual features over a span of roughly 5 6 h on two successive orbits of Venus Express. We find that the overall 1.74 lm brightness of Venus has been increasing through the first 407 days of the mission, indicating a gradual diminishing of the cloud coverage and/or thickness, and that the lower latitudes exhibited more variability and more brightening than higher latitudes. We find that individual features evolve with a time scale of about 30 h, consistent with our previous analysis. Analysis of the evolution and motion of the clouds can be used to estimate the mesoscale dynamics within the clouds of Venus. We find that advection alone cannot explain the observed evolution of the features. The measured vorticity and divergence in the vicinity of the features are consistent with evolution under the influence of significant vertical motions likely driven by a radiative dynamical feedback. We measure a zonal wind speed of around 65 m/s, and a meridional wind speed around 2.5 m/s by tracking the motion of the central region of the features. But we also find that the measured wind speeds depend strongly on the points chosen for the wind speed analysis. Ó 2011 Elsevier Inc. All rights reserved. 1. Introduction The two primary defining characteristics of the atmosphere of the planet Venus are its ubiquitous cloud cover that enshrouds the planet between the altitudes of about 50 km and 70 km, and the atmospheric super-rotation. The clouds have been shown to play a role in the maintenance of the present Venus climate (Pollack et al., 1980; Bullock and Grinspoon, 2001); and are often used as tracers to measure and interpret the global and local circulation of the atmosphere of Venus (Rossow et al., 1980; Piccioni et al., 2007; Sánchez-Lavega et al., 2008). However, observations of cloud dynamics show that the clouds of Venus cannot be considered homogeneous passive tracers of the dynamics at all altitudes (McGouldrick et al., 2008). Simulations show that the clouds of Venus can undergo significant variations on time scales ranging from hours to years, as a result of changes in local meteorology Corresponding author. address: kevin.mcgouldrick@lasp.colorado.edu (K. McGouldrick). (McGouldrick and Toon, 2007, 2008b), or as a result of climate change driven, for example, by surface chemistry (Bullock and Grinspoon, 2001). The clouds themselves and the atmospheric dynamics of Venus are intimately intertwined on both the global and local scales. How much does the venusian cloud cover vary, on both long and short time scales? What can the measured wind velocities in the vicinity of the evolving features tell us about the dynamical processes at work within the clouds? In this paper, we present an analysis of 1.74 lm radiance from Venus, measured by the Visible and Infrared Thermal Imaging Spectrometer (VIRTIS) on the European Space Agency s Venus Express spacecraft, to answer these questions by characterizing and quantifying the variations in the middle and lower cloud decks. 2. Background The clouds of Venus can be separated into three decks, with regions of diminished particle concentration, historically referred to as hazes, both above and below (Esposito et al., 1997). The /$ - see front matter Ó 2011 Elsevier Inc. All rights reserved. doi: /j.icarus

3 616 K. McGouldrick et al. / Icarus 217 (2012) composition of these clouds was found to be sulfuric acid (Sill, 1972; Young, 1973), while Hansen and Hovenier (1974) showed from polarimetry analysis that the particles that comprise the uppermost optical depth of the clouds are predominantly spherical. Subsequent analyses by Pioneer Venus polarimetry (Kawabata et al., 1980) and in situ particle size spectroscopy of the cloud particles (Knollenberg and Hunten, 1980) both confirmed these conclusions, and demonstrated that the deeper clouds (i.e., the middle and lower cloud decks) and the upper hazes also were likely to be composed of spherical droplets of sulfuric acid, with the possible exception of the largest mode of particles observed in the Large probe Cloud Particle size Spectrometer (LCPS) data. Knollenberg and Hunten (1980) suggested that these Mode 3 particles, with measured radii in excess of 3 lm might be non-spherical, or composed of a substance other than the sulfuric acid/water solution that comprises the majority of the Venus cloud system. But Toon et al. (1984) suggested that these larger particles may simply be a mis-measurement of the large radius tail of the Mode 2 size distribution. The composition and size of these controversial Mode 3 particles in the middle and lower clouds of Venus remains an unresolved question. The upper clouds of Venus are produced by photochemical reactions utilizing sulfur dioxide (SO 2 ) and water vapor (H 2 O) in the upper atmosphere of Venus (Winick and Stewart, 1980; Yung and DeMore, 1982). This results in a fairly uniform photochemical haze that is similar in terms of global coverage to the photochemical hydrocarbon haze on Titan. Radiative heating at the base of the clouds by upwelling infrared radiation from the warmer deep atmosphere and surface of Venus drives a radiative-dynamical feedback whereby convection draws fresh vapor upward where it can condense and contribute to the mass of the lower and middle clouds (Toon et al., 1982; James et al., 1997; McGouldrick and Toon, 2007). Consequently, the clouds play a significant role in the maintenance of the current surface conditions at Venus. Not only do the clouds reflect over 70% of the incident solar radiation, resulting in a planetary emission temperature lower than that of Earth, but the upper cloud layers are also the primary absorbers of the solar radiation that is not reflected, leading to significant solar heating at those altitudes, which is believed to be at least partially responsible for the energy that drives the global super-rotation (Schubert et al., 1980). Furthermore, the lower and middle cloud decks have been shown to be significant contributors to the venusian greenhouse effect, following in magnitude only CO 2 and H 2 O (Pollack et al., 1980). Thus, variability in the cloud layers will have a significant effect on the energy budget of Venus, and may have implications for the global atmospheric dynamics as well (Lebonnois et al., 2010). A characterization of that variability will be beneficial. In this paper, we explore the long term variation in the 1.74 lm night side emitted radiance as a descriptor of total cloud opacity. At visible wavelengths, the ubiquity and uniformity of the upper cloud is apparent. However, the presence of ultraviolet absorbers in these upper clouds (SO 2 and an as yet unknown species), reveals significant contrast at ultraviolet wavelengths that has been observed at least since the early 20th Century (Wright, 1927). The dark markings produced by these absorbers can be tracked while the atmospheric super-rotation carries them across the disk of the planet to give an accurate estimate of the wind speeds at an altitude of around 70 km (Rossow et al., 1980). However, until the 1980s, the only information about the global atmospheric circulation at altitudes below the upper clouds derived from Doppler tracking of descent probes, or radio occultations by spacecraft. Allen and Crawford (1984) discovered windows in the near infrared spectrum of Venus at wavelengths of 1.74 lm and 2.3 lm in which radiation emitted from the deep atmosphere of Venus is able to escape, impeded only by scattering by the cloud particles. The emitted light at these wavelengths was found to be highly variable both spatially and temporally. These variations were later shown to indicate significant opacity variations in the lower and middle cloud decks (Crisp et al., 1989). The identification of these features with the middle and lower clouds was supported by the tracking of these features, which travelled across the disk of the planet with velocities similar to the wind speeds that had been observed at the altitudes of the middle and lower clouds by in situ probes (Counselman et al., 1980). Subsequent work has utilized the ability to track cloud features at ever-improving spatial resolutions to attempt to characterize the nature of the global dynamics of Venus, including the global super-rotation, meridional flow, and the possibility of transient jets (Sánchez-Lavega et al., 2008; Peralta et al., 2008; Young et al., 2008; Hueso et al., 2012). However, microphysical modelling shows that because the lower and middle cloud decks are supported by a radiative dynamical feedback, they are capable of fairly rapid evolution, with time scales of hours to days (McGouldrick and Toon, 2007, 2008a,b). Subsequent preliminary analysis provides observational support to those claims (McGouldrick et al., 2008). If the edges of a feature are used to determine its motion, then morphological changes that the feature undergoes as its constituent cloud particles grow or evaporate with time can introduce errors into the measurement of the wind speeds. These morphological changes in the feature can also be strongly coupled with the local mesoscale dynamics in the vicinity of the feature. Thus, measurements of wind speeds utilizing the feature boundaries may more accurately sample the mesoscale dynamics that they do the global circulation. In this paper, we characterize the radiance evolution of individual cloud features in 1.74 lm images of Venus obtained by the VIRTIS-M-IR instrument on Venus Express, attempt to quantify the mesoscale dynamics in the vicinity of these cloud features, and investigate the existence of correlations between the local dynamical environment and the feature evolution. 3. Observational goals and methods The infrared channel of the VIRTIS-M instrument (VIRTIS-M-IR) is an imaging spectrometer with a spectral resolution of roughly k/ Dk 250, with 432 wavelength bins spanning a 1 5 lm spectral range. It can operate in a mode with a quarter of this spectral resolution, but we do not use the data from those observations, since the 1.74 lm window is very narrow. Our reasons for choosing the 1.74 lm window, as opposed to any of the other half-dozen or so other near infrared spectral windows, are described in McGouldrick et al. (2008). Summarized, we choose 1.74 lm because that wavelength affords us the best combination of brightness and contrast of the cloud features, and minimizes potential complications due to other radiatively significant species in the atmosphere. The orbital period of the Venus Express spacecraft is 24 h, so the number of orbits is equal to the number of Earth days. Thus, the 407 orbits that we analyze here comprise a little more than one Earth year. As the sidereal rotation period of Venus and the solar period of Venus are 243 days and 225 days, respectively, these data represent more than one full sidereal day, and more than one full solar year of Venus, meaning the data we are analyzing cover the full range of solar hour angles, as well as the full range of surface longitudes. The orbit of Venus Express also is highly elliptical and highly inclined. Apoapse is approximately 66,000 km, and periapse is about 250 km. The periapse has been subsequently reduced to about 150 km, but that had not yet occurred by orbit 407, which is the latest orbit considered here. The periapse of this highly inclined orbit occurs near the north pole of Venus.

4 K. McGouldrick et al. / Icarus 217 (2012) VIRTIS-M-IR can operate in two spatial resolution modes (0.25 mrad/pxl and 1.0 mrad/pxl). The former affords the ability to resolve features as small as about 16 km across when observing near apoapse, and about 10 km across when observing at a distance of 40,000 km from Venus. When the spacecraft is closer to Venus than this, it is moving too quickly for effective operation in imaging mode (Drossart et al., 2007). At apoapse, the field of view of the VIRTIS-M-IR instrument is such that a mosaic of approximately nine images is required to image the complete visible hemisphere. Since observations of Venus by VIRTIS-M in imaging or mapping mode are limited to times near apoapse, and due to the polar orbit of Venus Express as described above, the data analyzed here are from the southern hemisphere only. Thus, in the subsequent discussions and figures, all latitudes are assumed to be negative (i.e., in the southern hemisphere), unless otherwise noted Analysis of global trends We analyze VIRTIS-M-IR data from the first 407 orbits of the Venus Express spacecraft. Although the instrument was able to continue to collect data until the failure of the cooling system around orbit 920, we found that data following an observing strategy that was most useful for the current analysis become significantly more infrequent and sparse after this time. We applied a limb darkening correction to the 1.74 lm images, following Carlson et al. (1993) and Wilson et al. (2008): IðlÞ ¼I 0 ð0:316 þ 0:685lÞ 1 where I 0 is the measured radiance, l is the cosine of the emission angle, and I(l) is the corrected 1.74 lm radiance. We also remapped each image onto a cylindrical projection. We had intended to perform all of our analyses utilizing the channel that is closest to 1.74 lm. However, there are several known issues with the VIRTIS-M-IR data that prevented us from applying this simple approach. Among these are an odd-even effect and a shift in the spectral registration (Moinelo et al., 2010). The former involves gain differences between alternating spectels (pixels in the spectral dimension). This can be considered to be negligible in the case of observations such as those considered here, that have exposure times of three seconds or more (Moinelo et al., 2010). The latter is partly a known function of spectrometer temperature, but also involves changes that are not yet fully understood (Moinelo et al., 2010). This variation in spectral registration, whatever its cause, is more significant, as it will cause the peak of the 1.74 lm feature to drift among several channels of the detector. Because the 1.74 lm feature is so strong and narrow, it then becomes difficult to determine whether changes in measured radiance are due to actual changes in the emitted radiance, or due to changes in the sampled region of the feature. To minimize the effect of variations in the spectral registration on our measured radiances, we have calculated the spectrally integrated average radiance between 1.70 lm and 1.78 lm, a wavelength range that fully envelops the 1.74 lm feature and its wings (Fig. 1). Thus, any shifts in spectral registration will manifest themselves as changes in the contributions from the wings of the feature (at a level of only a few percent of the total), rather than a change in the contribution from the peak of the feature. To analyze the long term evolution of the cloud cover inferred from the 1.74 lm radiance, we obtained image cubes for each orbit in which at least part of the night side of Venus was observed. If the observations of the clouds were nested (i.e., successive images of the same region of the atmosphere) then we chose the image with the greatest spatial coverage of Venus (i.e., the cube obtained when the spacecraft was at its greatest distance from Venus during that ð1þ Fig. 1. The 1.74 lm feature in a hole ð}þ, and in a thicker cloud (). Note that an integration from 1.70 lm to 1.78 lm will capture most of the feature radiance, indicating that any shift of the feature location due to errors in the spectral registration will result in a relatively small error. orbit). If the observations from a given orbit were taken in a mosaic mode, then we analyzed the entire sequence of data. When the observations were a hybrid between these modes (e.g., a partial mosaic repeated several times), then we chose the set of mosaicked images that was obtained at the greatest distance (hence largest observed planetary fraction). In all cases, the goal is to maximize the fraction of the planet that is analyzed, and to minimize the amount of duplicated observations that might potentially skew the results. The analysis was performed only on regions of the planet that exhibited emission angles of less than 85 to avoid potential problems with the limb darkening correction. Furthermore, only regions experiencing solar incident angles of greater than 95 (i.e., the Sun is at least 5 below the horizon) are considered, in an attempt to eliminate contributions due to sunlight scattered onto the night side of the planet Analysis of individual features On the other hand, to analyze the evolution of individual features, we searched for observing sequences in which the successive images of the clouds were nested. Since the field of view of the VIR- TIS-M-IR instrument subtends only a fraction of the planetary disk, it is unlikely that the evolution of individual cloud features could be analyzed across multiple orbits unless the pointing was specifically directed and very fortunate. The motion of the feature and the morphological evolution of the feature would be too great. In order to standardize our definition of a feature, we defined radiance contours in increments of 0.02 W m 2 lm 1 sr 1, and identified features as regions that were fully enclosed by one of these contours. Each feature is identified either as a cloud (local minimum in radiance) or a hole (local maximum in radiance); these definitions hold throughout this paper. If the feature was clipped by the observed field of view, we disregarded it. Sometimes, a feature exhibited radiance so significantly different from its surroundings that multiple closed contours could be defined. In the interest of simplicity, for the current analysis we considered only the innermost contour. This probably introduces a bias toward the analysis of only the smaller features that exist in the observations. However, since the limited field of view already introduces a bias against larger features, we concluded that the additional bias introduced by this selection effect would be insignificant by comparison. Oftentimes (but not always), the region defined by the larger

5 618 K. McGouldrick et al. / Icarus 217 (2012) contour would be clipped by the field of view after only an image or two, which would have disqualified the feature anyway, since we limited our analysis to only those features that persisted across a minimum of three images in the observational sequence. Finally, it is important to note that although our primary goal is the analysis of bright cloud features (holes), we did not discriminate against the dark features (localized cloudy regions) that appeared in the images. If a dark region met the above criteria, its evolution, too, was analyzed. We obtain geographical and radiative information about each feature. We collect the value of the peak in the radiance of each feature, as well as its geographic latitude and longitude. For a bright region (hole), the peak radiance is defined as the location where the 1.74 lm radiance within the closed contour reaches a maximum. For a dark region (cloud), the peak radiance is defined as the location where the radiance reaches a minimum. We also calculate the total integrated radiance of each defined feature. In addition to this radiance information, we also collect the latitude and longitude of the easternmost, westernmost, northernmost, and southernmost points of the identified features. We obtain this information for every feature that we can identify by our above criteria, for each image. Any feature that was identifiable across a minimum of three images was then included in the evolution analysis. To analyze the evolution of the peak radiance of these features, we assume an exponential relationship between peak radiance and time. Specifically, we find linear fits to the natural logarithm of the radiance as a function of time: ln I peak ðtþ ¼ln I peak ð0þ 1 s rad t where the evolutionary time constant [s rad ] and the initial peak radiance [lni peak (0)] are free parameters that are fit to the data for each feature. To analyze the effect of this evolution of the feature on the measured wind speeds, we determine the wind speeds associated with tracking of the five main points that we identified in each feature: east, west, north, south, and peak. We calculate the wind speeds by finding a best linear fit to the position of these defined points with time: lonðtþ ¼lonð0Þþu t latðtþ ¼latð0Þþv t ð2þ ð3þ ð4þ where [lon(0), lat(0)] (the initial longitude and latitude), and [u, v] (the zonal and meridional velocity components) are free parameters to be fit to the data. We have performed these analyses for two sequences of observations; a summary of the information for each is included in Table 1. Orbit 383 consists of a sequence of 11 cubes obtained in intervals of 30 min, beginning at 2028UTC on 8 May Orbit 384 consists of a sequence of 12 cubes obtained in intervals of 30 min, beginning at 2032UTC on 9 May Each observation utilized an exposure time of 3.3 s per frame. These two image sequences cover a range of geographic latitudes and longitudes that are nearly identical to one another. The only thing that differs significantly is that the atmosphere and clouds have experienced 24 h of advection and evolution. Based on both in situ Doppler-tracked measurements from Pioneer Venus (Counselman et al., 1980) and Vega (Sagdeev et al., 1986), and remote observations in the near infrared by Venus Express (Sánchez-Lavega et al., 2008), the equatorial and mid-latitude atmosphere at these altitudes rotates about the planet with a period of approximately 7 days (a velocity of about m/s near the equator). As a result of this, the features we are analyzing in this paper are from two distinct regions of the atmosphere that are separated by approximately 50 of longitude (i.e., a distance of about 4600 km at 30 latitude). Nevertheless, a significant amount of variation can be seen from even an initial glance at these two sets of observations (Fig. 2). 4. Results 4.1. Long-term global variations To analyze the long-term and global variations in cloud cover from 1.74 lm radiance, we first plot the zonally averaged radiance as a function of latitude and time for all of the analyzed images (Fig. 3a). Because the data from any one orbit can reveal information about no more than a quarter of the planet in this analysis, we also plot the same data in Fig. 3b, but in which we apply a seven day moving window average (analogous to the roughly 7-day atmospheric super-rotation period) to the data to emulate a global averaging of the observations. Several things are apparent in these figures. First, there is a clear latitudinal variation in radiance, with a significant and persistent minimum near 70 latitude, consistent with the location of the polar collar. There is also an evident local maximum in the radiance at a latitude of about 50, indicating a minimum in cloud thickness and/or coverage at these latitudes. This is consistent with earlier observations (Crisp et al., 1991; Tavenner et al., 2008; McGouldrick et al., 2008; Barstow et al., 2012) and models (Imamura and Hashimoto, 1998) of the Venus clouds. Furthermore, the equatorial regions appear to exhibit far more variation in 1.74 lm radiance than the rest of the planet, a suggestion that is made more apparent by comparison with the plot of the standard deviation about the mean 1.74 lm radiance, as shown in Fig. 4. Finally, it appears Table 1 Observations used to analyze feature evolution. Image VIR0383 VIR0384 Start time (Y-M-D, time (UTC)) Lat. range ( ) Lon. range ( ) Start time (Y-M-D, time (UTC)) Lat. range ( ) Lon. range ( ) , to , to , to , to , to , to , to , to , to , to , to , to , to , to , to , to , to , to , to , to , to , to N/A N/A N/A , to

6 K. McGouldrick et al. / Icarus 217 (2012) Fig. 2. VI0383_00, VI0384_00. Comparison of two re-mapped 1.74 lm images from VIRTIS-M-IR, showing the dramatic difference between cloud coverage and appearance over a time of only 24 h. These images have been re-mapped onto a cylindrical projection, so that north is up and east is to the right. Animated GIFs of the 11-image sequence for orbit 383 and the 12-image sequence for orbit 384, with radiance contours overlaid have been provided as Supplementary material viewable in the online version. as though the planet as a whole has increased in 1.74 lm radiance over the course of the first 407 days of the mission. In order to quantify this perceived increase in radiance (hence decrease in cloud coverage and/or thickness), we show in Fig. 5a the average radiance, spectrally integrated from 1.70 lm to 1.78 lm, for each analyzed orbit, as well as linear best fits to the data. In addition, because there appear to be significantly distinct geographical regions in the atmosphere of Venus that exhibit somewhat differing tendencies in radiance with time, we also show these integrated average radiances for three distinct latitude ranges, equatorial (0 30 ), mid-latitude (30 60 ), and polar (60 90 ), in Fig. 5b d, respectively. Most noticeable is the significantly greater variability exhibited by the equatorial region. Also apparent in these figures is the positive slope to the mean radiance over the course of the 407 days analyzed here. It is more apparent in the equatorial region, and is much smaller in the polar region, but in each of the four cases, the average trend to the mean radiance is positive (column 1 of Table 2). However, since a fair amount of short-term variation is also evident in these figures, it may be that this apparent trend is merely the consequence of random fluctuations. To that end, we have also calculated the uncertainty in the estimated best fit parameters. It is evident from Table 2 that although the trends in overall radiance are positive, both regionally and globally (in the southern hemisphere), it is also clear that the magnitude of this trend is Fig. 3. Zonally averaged 1.74 lm radiance as a function of latitude for the first 407 orbits of the Venus Express mission. shows data averaged per orbit, shows the same data smoothed with a seven-orbit moving window. smaller than the standard deviation of the fit in nearly every case. Thus, many of these variations are consistent with the case of no change in radiance over this time. However, two aspects of the analysis of Fig. 5 and Table 2 prove interesting. First, the equatorial regional average, which exhibits the largest amount of overall variability in radiance, also exhibits the largest trend in radiance. The third column of Table 2 calculates the total change in radiance over the course of the 407 days analyzed here by multiplying the slope (based on the best fit estimate) by 407 orbits. The total change in the spectrally and regionally averaged radiance in the equatorial region is approximately 35%. This is considerably larger than the 4 8% changes measured in the other three regions and the 4 8% error estimates on I 0 for all four regions. Since these 4 8% variations are comparable to the errors expected from variations in the spectral registration, based on the analysis of Fig. 1, we conclude that the calculated linear trends in all regions except the equatorial region are likely negligible. The second interesting point in these data is the behavior of the mid latitude spectrally averaged radiance (Fig. 5c). A sinusoidally varying trend line with a period of 140 days, a magnitude of 0.01 W m 2 sr 1 lm 1, and an average value of 0.05 W m 2 sr 1 lm 1 is additionally plotted in that figure. This curve appears to do a very good job of matching the variations seen in the mid latitude radiance. The period and magnitude are estimated by eye, and a thorough analysis of the possible frequency spectrum has not yet been carried out, so this is a qualitative estimate only. Nevertheless, the fit appears to be consistent enough to warrant future investigation. This period of 140 days

7 620 K. McGouldrick et al. / Icarus 217 (2012) Fig. 4. Standard deviation about the zonally averaged 1.74 lm radiance as a function of latitude for the first 407 orbits of the Venus Express mission. shows data averaged per orbit, shows the same data smoothed with a seven-orbit moving window. cannot be explained by geographical variations (243 days) or hour angle variations (225 days). If real, another driver must be postulated to explain this variation Radiance evolution of individual cloud features In Fig. 6, we show the best fit evolutionary time scale (s rad )of each analyzed feature plotted against the best fit peak radiance. In this and subsequent figures, we indicate whether the analyzed feature is a cloud (local minimum in radiance: ) or a hole (local maximum in radiance: }). Large error bars in s rad usually indicate that a feature has both expanded and contracted. That is, the feature may have grown brighter to some maximum radiance, then subsequently proceeded to grow dimmer during the course of the analyzed observation sequence. Such behavior obviously will not be fit well by a monotonously linear fit to the log of the radiance. Compared with our previous paper on the evolution of features in the Venus clouds (McGouldrick et al., 2008), we have improved our feature identification techniques and our calculation of feature evolution time scales. An examination of Fig. 6 suggests a lack of a correlation between feature peak radiance and feature lifetime. This is in contrast with the suggestion of McGouldrick and Toon (2008a) that features with greater contrast (more brightness, hence less cloud than the surrounding region) would evolve more quickly, based on their models of a single large (2000 km) hole in the Venus clouds, and the analysis of McGouldrick et al. (2008) tentatively agreed with this supposition. However, such a relationship is not as apparent in the current analysis, perhaps because the features that we are analyzing here (nearly all of which are smaller than 1000 km; approximately half are smaller than 400 km) are somewhat smaller than those simulated by McGouldrick and Toon (2008a), and also exhibit somewhat smaller contrast (note that the cloud and hole spectra in Fig. 1 differ by a ratio of approximately 5:1). The differences between these results and those of McGouldrick et al. (2008) can be attributed to our improvements in consistent feature identification from image to image, and to our now defining features according to a consistent set of radiance contour levels. We plot the calculated evolution time scale versus the latitude of the features in Fig. 7. The latitude is defined by the location of the peak radiance of the feature, as defined in Section 3. As with the comparison between evolution time scale and peak radiance, there does not appear to be an obvious trend on feature evolution with latitude. This also is in contrast with our previous paper, which identified a possible latitudinal dependence that required further analysis to confirm. Although this work represents only one additional (but very different) sequence of observations, it appears that the subtle latitudinal and radiance dependencies of the feature evolution time scale that were perceived in our previous analysis are not significant. Finally, in Table 3, we summarize the cloud feature evolution time scale analysis. We separate the summary according to orbit, feature type, and mean versus absolute time scales. The latter distinction indicates whether the sign of the evolution time scale was considered in the calculation of the mean. For example, two features with time scales of +12 h and 12 h (i.e., one grows, and the other diminishes with the same 12-h time scale) have a mean time scale of 0 h and an absolute time scale of 12 h. We find that the typical absolute time scale for evolution of features in the clouds of Venus is approximately 30 h. This is consistent with, but slightly longer than the 1 day time scale reported in McGouldrick et al. (2008). Interestingly, this mean time scale is approximately the same for both bright and dark features, and across the two orbits worth of data analyzed here, despite the significant differences in the appearance of the planet at these two observation times that suggest significant differences in the cloud formation conditions. Evidence for changes in the cloud formation conditions between the two orbits may also be evident in these data. The observation from orbit 383 exhibits significantly greater numbers of bright features and relatively large fractional coverage of brighter regions (especially at equatorial latitudes) than does the observation from orbit 384. Table 3 indicates that the bright features observed in the images from orbit 383 are typically growing brighter (positive mean time scale), while in the images from orbit 384, they are growing darker (negative mean time scale). For each orbit, the mean time scale for dark features is fairly small, despite an absolute time scale that is consistent with that of the bright features, indicating that there are numerous growing dark features as well as numerous diminishing dark features in these data Mesoscale dynamics in the vicinity of cloud features As discussed in Section 2, the individual cloud features themselves are undergoing significant variation due to local dynamics and meteorology. We now compare several diagnostics of the meteorological environment in the vicinity of individual features with their measured evolution. In Fig. 8, we plot the zonal velocity of the eastern point minus that of the western point for each feature for each of the two orbits considered in the present analysis. In the following discussion, we will refer to this difference as the wind differential. Since, given the global super-rotation, the zonal velocity of the entire atmosphere is from the east, all of the

8 K. McGouldrick et al. / Icarus 217 (2012) (c) (d) Fig. 5. Long-term variation of the spectrally integrated radiance within the 1.74 lm feature over the first 407 days of the Venus Express mission, for four defined geographic regions: the entire southern hemisphere (0 90 ), equatorial latitudes only (0 30 ), (c) mid latitudes only (30 60 ), (d) polar latitudes only (60 90 ). In each plot, the calculated average radiance is shown with symbols (+), and a linear best fit trend line is plotted over the data. Also indicated by a dashed curve in (c) is a sinusoidally varying radiance with a period of 140 days that appears to match the variations seen in the mid latitude data. Table 2 Long-term trends in 1.74 lm radiance. I 0 (W m 2 sr 1 lm 1 ) di/dt (10 6 Wm 2 sr 1 lm 1 ) di/dt 407 (W m 2 sr 1 lm 1 ) Hemisphere ± ± ± Equatorial ± ± ± Mid latitude ± ± ± Polar ± ± ± measured zonal velocities (except in the possible case of a very rapidly evolving feature) are negative. Hence, a negative wind differential (U e U w < 0) indicates that the eastern edge of the feature appears to be travelling more quickly to the west than the western edge of the feature. In each orbit that we have analyzed here, the number of points with DU < 0 exceeds the number with DU >0, although the effect is significantly more pronounced in the data from orbit 384. The existence of a significant zonal wind differential also suggests that advection, likely driven by vertical shear of the zonal winds, could be responsible for the evolution of the near infrared cloud features. In these data, there appears to be a trend in which larger zonal wind speed differentials are correlated with more negative feature evolution time scales. Similarly, smaller zonal wind speed differentials are correlated with more positive evolution time scales. Recall that a negative zonal wind differential implies that the eastern edge of the feature is measured to have a greater speed than the western edge of the feature. In order to determine whether there is a relationship between the evolution of the feature and the zonal wind speed differential, we compare the zonal wind speed differential and the evolutionary time scale from the peak radiance for each feature analyzed in Fig. 9. This plot indicates that for features that are diminishing with time, one is more likely to measure a eastern zonal wind speed that is considerably faster than the western zonal wind speed. Similarly, for a feature that is growing over time, the opposite is true: the western speed typically will be measured to be greater. This implies that a diminishing feature is eroded preferentially from the east, or the trailing side of the feature, while the features tend to grow in a westward direction. We calculate a simple advective time scale for the destruction of a feature due to the zonal wind differential at the edges of the feature (s adv = dx/du), where dx is the longitudinal size of the feature, and DU is the zonal wind differential. In Fig. 10, we compare the advective time scale of feature evolution with the time scale associated with the peak radiance of the features that was discussed in Section 4.2 and Fig. 6. It is clear that the advective time scales are much shorter than the radiance evolution time scales. In fact, many of the analyzed features should have been completely eradicated by advection during the course of the observations being analyzed, since they have time scales of less than the six hour duration of the observation sequence. Most of the features that remained within the instrument field of view for the duration of the sequence of observations also were seen to persist through the entire sequence. Hence, we conclude that while advection of feature edges due to shear is significant, it is not the primary driver of feature evolution. Similarly, in Fig. 11, we plot the meridional wind differential for each feature. In this case, there is again a clear trend in which nearly all of the features observed in orbit 384 indicate meridional

9 622 K. McGouldrick et al. / Icarus 217 (2012) Fig. 6. Derived evolution time scale of each feature plotted versus measured peak radiance of the feature for orbit 383 and orbit 384. Holes are indicated by ð}þ, clouds are indicated by (). The lines associated with each feature indicate the standard deviation of the best fit to each derived parameter. Fig. 7. Derived evolution time scale of each feature plotted versus measured latitude of each feature for orbit 383 and orbit 384. Holes are indicated by ð}þ, clouds are indicated by (). The lines associated with each feature indicate the standard deviation of the best fit to each derived parameter. velocities that are more northward on the western edges of the features, while the observations from orbit 383 indicate (albeit less obviously) that the eastern edges of the features have a more northward velocity. Note that, unlike the zonal case, there is not necessarily a consistent direction to the flow in the meridional direction. Consequently, it is not clear from this particular figure whether a positive DV indicates that the western edge is travelling northward at a greater velocity, or that the western edge and eastern edge are travelling in opposite directions. Regardless, together, the measured DV and DU do suggest an orientation of the vorticity for each feature. Fig. 8 through Fig. 11 suggested a possible connection between the radiance evolution of the individual features and the local divergence and vorticity in the vicinity of those features. We now consider that potential relationship in more detail. Divergence is defined as r V, while horizontal divergence Thus, a positive value of horizontal divergence or a separation of the air parcels, while a negative value of horizontal divergence indicates convergence or a coming together of the air parcels. In Fig. 12, we compare the horizontal divergence calculated from the measured wind speeds in the vicinity of the features with the radiance evolution time scale derived in Section 4.2. There is a clear segregation of the holes in these figures. All of the analyzed holes (radiance maxima, i.e., clearings in the clouds), are found in either the upper right or lower left quadrant. That is, every hole that is growing brighter with time also exhibits horizontal divergence, while every hole that is growing Table 3 Summary of evolution time scale analysis. VIR0383 Mean s rad (h) Absolute s rad (h) VIR0384 Mean s rad (h) Absolute s rad (h) All features Holes only Clouds only darker with time also exhibits horizontal convergence. Conversely, every dark feature but two is found in one of the other two quadrants (darkening clouds exhibit divergence while brightening clouds exhibit convergence), and even those two that buck the trend have error bars that extend into those quadrants. Vorticity, on the other hand, is defined as r V, the vertical component of which Thus, a negative vorticity indicates counter-clockwise flow, while positive vorticity indicates clockwise flow. In Fig. 13, we compare the vorticity in the vicinity of each feature with its radiance evolution time scale. The situation here is somewhat less clear than in the divergence case, as approximately half of the analyzed features exhibit vorticity error bars that cross the zero line. Hence, it is difficult to determine whether the feature exhibits a clockwise or a counter-clockwise circulation. Nevertheless, it does appear as though holes that exhibit counter-clockwise flow also become darker with time, while

10 K. McGouldrick et al. / Icarus 217 (2012) Fig. 8. Zonal wind differential for each feature, where zonal wind differential is defined as the difference between the zonal velocity measured for the eastern edge minus that measured for the western edge. Orbit 383; orbit 384. Holes are indicated by ð}þ, clouds are indicated by (). The lines associated with each feature indicate the standard deviation of the best fit to each derived parameter. Fig. 9. Zonal wind differential plotted versus evolution time scale for each image in orbit 383; orbit 384. Holes are indicated by ð}þ, clouds are indicated by (). The lines associated with each feature indicate the standard deviation of the best fit to each derived parameter. those exhibiting clockwise flow tend to grow brighter with time. Conversely, dark features exhibiting clockwise flow grow darker with time while those exhibiting counter-clockwise flow tend to grow brighter with time. In Fig. 14, we compare the vorticity and divergence of each analyzed feature. More noticable in the data from orbit 384, but evident in both, is the relationship between vorticity and divergence that exists both for holes and darker cloud features. For nearly every feature analyzed, convergence coexisted with counter-clockwise vorticity, while divergence coexisted with clockwise vorticity. Only two features from the two orbits analyzed are inconsistent with this pattern. Finally, in Table 4, we compare the average measured wind speeds for our five chosen points for each feature from each of the two analyzed orbits. The most consistent measured wind speeds between the two orbits considered here are derived from analysis of the motion of the peak radiance of each feature. In each case, the zonal wind speed of the peak of the feature was measured to be around 65 m/s from the east to the west, while the meridonal wind speed was measured to be about 2.5 m/s from the south to the north. This meridional wind speed is consistent with the wind speed derived by Crisp (1989) from radiative equilibrium calculations, and with the meridional wind speed used by Imamura and Hashimoto (1998) to estimate the effects of the meridional circulation on the Venus cloud system. It is also consistent with the meridonal wind speeds measured by Sánchez-Lavega et al. (2008) from VIRTIS-M-IR observations between April 2006 and July 2007, and more recent work by Hueso et al. (2012), who analyzed observations from April 2006 through August As all of the analyzed features were located in the southern hemisphere, the positive meridional velocities indicate that the features are travelling, on average, toward the equator. That the magnitude of the observed meridional flow is similar to that of the Hadley circulation predicted by Crisp (1989) indicates that the return branch of the Hadley circulation lies primarily within the clouds. If the return branch were to be located at altitudes somewhat lower than those at which the clouds are found, then the magnitude of the meridional flow that we would be able to track from cloud motions would be somewhat smaller. However, also consistent with the work of Sánchez-Lavega et al. (2008) and Hueso et al. (2012), our error estimate for the meridional velocity is larger than the magnitude of the measured meridional wind itself, indicating that the Hadley circulation, if it exists at the altitude of the middle and lower clouds, is not the dominant driver of meridonal motion there. 5. Discussion The gradual increase in 1.74 lm radiance indicated by Fig. 3 and Table 2 can be explained by a gradual decrease in overall cloud coverage and/or thickness in the atmosphere of Venus during the first 407 days of the Venus Express mission. There are a number of possible explanations for such a change. The sulfuric acid that comprises the clouds of Venus is produced by photochemistry involving SO 2 and H 2 O in the upper atmosphere. If the amounts

11 624 K. McGouldrick et al. / Icarus 217 (2012) Fig. 10. Evolution time scale calculated from zonal wind differential for each feature, compared with the evolution time scale calculated from the variation in the peak feature radiance. Orbit 383; orbit 384. Holes are indicated by ð}þ, clouds are indicated by (). The vertical lines associated with each feature indicate the standard deviation of the best fit to the radiance evolution time scale, while the horizontal lines indicate the error estimates on the advection time scale, based on error estimates of the measured wind speeds. of these components diminish, the mass of the upper clouds will also necessarily diminish. The sulfuric acid aerosol particles grow over time, allowing them to fall to deeper levels of the atmosphere. The sulfuric acid vapor evaporates and is then thermally decomposed, ultimately back into its primary components, SO 2 and H 2 O. This results in a net transport of SO 2 from the upper atmosphere to the lower atmosphere. Unless there is a mechanism (e.g., Hadley circulation, eddy diffusion, or volcanism) for completing the cycle by returning SO 2 to the upper atmosphere, this will result in a gradual diminishing of cloud mass as the lower atmosphere acts as a sink for sulfuric acid. Thus, this reduction in cloud cover deduced from the changes in the 1.74 lm radiance may be indicative of either an absence of significant volcanism over the course of the Venus Express mission, or an inefficiency in the ability of Hadley circulation or eddy diffusion to bring SO 2 from the deep atmosphere of Venus back to the photochemical layer. The time scale for the observed reduction in cloud coverage is too rapid to be explained by geochemistry (Bullock and Grinspoon, 1996). Another possibility is that we are seeing the effects of a severalyear-long variation in the atmospheric microphysics and dynamics. For example, decadal-scale variations have been seen in recent General Circulation Models of Venus (Parrish et al., 2010). The data analyzed here represent just over 1 year of observation, which is too brief a time to discern changes having a ten-year period. Fig. 11. Meridonal wind differential for each feature. Orbit 383; orbit 384. Holes are indicated by ð}þ, clouds are indicated by (). The lines associated with each feature indicate the standard deviation of the best fit to each derived parameter. However, the full data set of VIRTIS-M-IR observations stretches over a time baseline of approximately 2.5 years. This, being one quarter of a ten year period, may be just sufficient to notice effects of a ten-year periodicity, should they exist. Future investigations will involve attempts to correlate these 1.74 lm variations with measurements of upper atmospheric SO 2, as measured by VIRTIS- M-Vis, a project that the author is presently carrying out; but the results of which are too premature for publication at the present time (McGouldrick et al., 2011). The time scale for feature evolution was found to be 30 h. How does this compare with other expected time scales in the Venus atmosphere? An eddy diffusion coefficient of about 250 m 2 s 1 corresponds also to a 30-h time scale, if the mixing region is taken to be about 5 km. This is consistent with the range of eddy diffusion coefficients utilized by previous microphysical simulations (James et al., 1997; Imamura and Hashimoto, 2001; McGouldrick and Toon, 2007). Thus, this observed time scale is consistent with parameterizations of convective motions in the lower and middle clouds of Venus that have been used by previous modelling studies. Another possibility is that the feature evolution may be driven by the gravitational sedimentation of the cloud particles. A cloud particle having a radius of 1 lm, typical of the Mode 2 particles, will have a gravitational sedimentation velocity of around 0.03 cm/s. Hence, such a particle will fall only about 30 m in 30 h time. Similarly, a particle with a radius of approximately 3 lm, consistent with the particle size reported by Knollenberg and Hunten for Mode 3 particles, exhibits a sedimentation velocity of about