THE MORPHOLOGY AND PROCESSES OF A DEEP, MULTI-LAYERED ARCTIC CLOUD SYSTEM

Transcription

1 THE MORPHOLOGY AND PROCESSES OF A DEEP, MULTI-LAYERED ARCTIC CLOUD SYSTEM M. Rambukkange 1, J. Verlinde 1, P. Kollias 2,and E. Luke 3 1 Penn State University, University Park, PA 2 McGill University, Montreal, Quebec 3 Brookhaven National Lab, NY 1. INTRODUCTION Mixed-phase clouds play an important role in the Arctic climate system. These clouds occur frequently in the spring and fall seasons (Pinto 1998; Intrieri et al., 2002), and also have been observed to persist for long periods of time (12 hours to days, Shupe and Matrosov, 2006). Mixed-phase clouds have been observed in temperature ranges from -3 0 C and C (Witte 1968; Hobbs and Rangno, 1998; Pinto and Curry, 2001; Intrieri et al., 2002). These clouds usually contain a super-cooled liquid layer near the top with precipitating ice particles (McFarquhar et al., 2007; Intrieri et al., 2002; Pinto and Curry, 2001; Hobbs and Rangno, 1998). Despite their prevalence, detailed microphysical and dynamical descriptions of the processes involved in their formation and maintenance are not well known (Verlinde et al., 2007). Mixed-phase cloud strongly impacts the surface energy budget in the Arctic (Persson et al, 2002). The difference in the size, shape, and refractive index between water drops and ice particles result in significantly different radiative properties (Sun and Shine, 1994). In particular, Sun and Shine (1994) demonstrated the necessity for accurately specify the liquid water content by showing that greater errors result when all ice is converted into liquid compared to totally ignoring the ice phase in the radiative transfer calculation. However, the ice phase determines the liquid water amount and longevity in mixed-phase cloud by acting as a sink (Harrington et al., 2001). Therefore, it is important to include mixedphase clouds in climate models to reproduce the present climate realistically (e.g. Gregory and Morris 1996). Current mixed-phase cloud parameterizations need to be improved to obtain higher levels of confidence in climate model simulations (Gregory and Morris, 1996). For example, McFarquhar et al. (2007), using in situ observations of the fine scale structure and cloud properties of mixed-phase clouds in the Arctic, showed that current climate model parameterizations which specify the liquid water fraction as a function of temperature does not match observations taken during the Mixed-Phase Arctic Cloud Experiment. The Mixed-Phase Arctic Cloud Experiment, conducted in October 2004, contains a rich set of data capable of being used for a detailed study of mixed-phase clouds (Verlinde et al., 2007). In this paper we present an analysis of Doppler velocity spectra from the DOE-ARM millimeter wavelength cloud radar (MMCR). Shupe et al. (2004) showed how Doppler spectra may be used to identify the phase of, and quantify the microphysical characteristics of hydrometeor populations in mixed-phase clouds. The most recent work using Doppler spectra data from the Millimeter Wavelength Cloud Radar focused on obtaining vertical velocities and turbulent dissipation rates (Shupe et al., 2007). This study focuses on a detailed analysis of the Doppler spectra data to improve our knowledge of the microphysical structure, ice formation mechanisms and maintenance of liquid layer in Arctic mixed-phase stratus clouds.

2 Figure 1: (a) Doppler velocity spectrum from a single range gate. The velocity of the slowest hydrometeor is found at the left edge of the spectrum. By convention use in radar meteorology, negative velocity is upward. (b) Stacking velocity spectra in time. (c) Stacking velocity spectra in height. (d) Time series plot of spectra at a single range gate: color shading represent spectral reflectivity. (e) Spectrograph obtained at single time.

3 2. DOPPLER VELOCITY SPECTRA A Doppler radar spectrum is the hydrometeor backscatter power (or reflectivity) distributed in radial velocity (Fig. 1a). These spectra can then be aligned in time and velocity to obtain a spectral time series plot (Fig. 1d) which provides information about the time evolution of spectra at a particular height. Alternatively, they can be combined in height and velocity to yield a spectrograph (Fig. 1e). This point of view is ideal for keeping track of different hydrometeor modes and their velocity in height. For example, in Fig. 1e precipitation that fall into the cloud at 2 km clearly grows into three distinct modes below the cloud layer (denoted by A, B, and C). Doppler radar spectra can also be used to extract additional properties such a total reflectivity by taking its moments. The zeroth spectral moment of the Doppler spectra yields the signal mean power ( integral of the spectral reflectivity yields total reflectivity), the first moment gives the mean Doppler velocity, and the second moment specifies the spectral variance (square root of this quantity is called the spectrum width). In the presence of a liquid clouds the vertical pointing MMCR measures the vertical velocities of the cloud drops which are the sum of their quiet-air terminal fall speed and air motions. These air motions can be divided into a radar volume-mean velocity that can result in shifting the whole spectrum and fluctuating part (turbulent) that can act to broaden the quiet-air spectrum (Babb et at., 1999). The quiet air terminal fall speeds of typical cloud drops (~ µm) is less than 2 cm s -1. Therefore in the presence of air velocities much larger than their terminal fall speed, drops can act as tracers of air motions, but can t be directly used to estimate drop size distribution (Gossard et al., 1997). Under this condition the slowest hydrometeor fall speed obtained from the Doppler spectrum gives the air velocity in the presence of cloud (Fig. 1a). 3. SYNOPTIC SITUATION AND DATA The synoptic condition over North Slope of Alaska (NSA) during Oct 4-8 th was mainly controlled by a high pressure that was located over the ocean north of Barrow, Alaska. A weak disturbance that originated over the Eastern Brooks Range moved over the ocean and then moved along the coast prior to its dissipation over Deadhorse. Though the low pressure system did not cause much change in the surface winds and temperature fields, it carried with it sufficient moisture at mid level and upper levels to cause cloudiness over NSA. These upper-level clouds along with the boundary layer stratus caused the multi-layered decks that were seen over Barrow during October 6 th (Yannuzzi, 2007). The soundings at 1059 UTC and 1659 UTC were used to estimate temperature and wind information at 1300 UTC. A constant wind speed of 5 ms -1 flowing from east of northeast was observed between 2km and 3 km on both soundings, and also captured by the Eta model surface analysis. Therefore this wind speed will be used as wind speed at 1300 UTC. The data used for these analyses were collected by the High Spectra Resolution Lidar (HSRL) data and the 35 GHz, Millimeter Wavelength Cloud Radar (MMCR) at the NSA Atmospheric Radiation measurement (ARM) climate research facility during the Mixed Phase Arctic Cloud Experiment (M-PACE), that was conducted during 27 September 22 October 2004 (Verlinde et al. 2007). The regions of high aerosol backscatter cross section and near zero HSRL linear depolarization are in good agreement; therefore a plot of the aerosol backscatter is not included in this study 4. Results In Fig. 2 we present the thermodynamic profile and an instantaneous spectrograph representative of the cloud overhead the radar. The layer between 0.5 km and 2 km reveals clear bimodality in the spectra, from

4 which one can deduce that there are two distinct populations of hydrometeors in the radar volume. The mean velocity for each of these modes differ: e.g. at 1.86 km in Fig. 2b, the mean velocity of the slow-falling mode (indicated as liquid cloud layer ) is close to 0.3 m s -1 whereas the mean velocity of the fast-falling mode (precipitation ) is about 1.0 m s -1. Therefore, the horizontal size of the domain shown is about 2.5 km. The reflectivity plot reveals evidence of vertical shear of the horizontal wind, seen as slanted streaks of maximum/minimum reflectivity. The velocity plot reveals several sharp discontinuities in height, particularly at 0.5 km and 2 km, and discontinuities in time between 3.5 km and 4 km. The linear depolarization plot shows near-zero valued layers (liquid layers) corresponding to the velocity features at 2 km and close to cloud top at 4 km, but not in the lower layers which are dominated by heavy precipitation. The higher depolarization valued regions between these layers contain precipitating ice. One may thus interpret the 2 km layer of slow moving hydrometeors as a liquid cloud layer (indicated as liquid layer in Fig. 2b. The velocity of the slowest hydrometeor may therefore be used to identify imbedded liquid layers in precipitating ice which otherwise are not visible in the reflectivity plot. The MWR plot confirms the correctness of the interpretation of these layers as liquid layers. In Fig. 4 we present a more complete view of the evolution of this imbedded liquid layer through time-series plots of spectra at various heights spanning the cloud layer at 1.9 km. Layers well above cloud top (a & b), spanning the cloud (c & d), in the ice precipitation below this cloud layer (e & f), the analysis period and is indicated as liquid in Fig. 4d. In contrast, the slow falling mode in Fig 4e is precipitating ice originating in the liquid layer above. The reflectivity of the ice mode precipitating through the liquid layer increases (seen at 1.9 km in Fig. 2b and also in Fig. 4c & 4d), likely the result of riming which increases the density, and hence the reflective index and the evolution of the two modes in height (g & h) are shown. Fig. 2a reveals that the altitudes spanning these layers have a generally stable temperature profile, although aircraft measurements (not shown) revealed that each liquid layer typically is found in a shallow mixed layer capped by an inversion. Looking at the mean velocity in the continuous precipitation mode, one can seen regular variations on the order of 0.7 m s -1, and period about 4 minutes, at 2.5 km, becoming more damped at lower altitudes. We speculate that these variations are gravity waves forced by the radiatively driven convection in the top most cloud layer at 4 km, evidence of which can be seen from the fluctuations in speed of the slowest falling hydrometeors (Fig. 3b). Fig. 4a reveals the presence of broken cloud (liquid) at 2.5 km (between and UTC) with vertical motion close to 0 m s -1, from which one can deduce the mean fall speed of the precipitating ice at that altitude to be 0.75 m s -1, the difference between the air motion and the mean velocity of the precipitation mode. The liquid cloud layer identified in Fig. 3b & 3c can easily be distinguished from ice below cloud base using a spectrograph. Below the base of the liquid layer as indicated by the HSRL a sharp drop-off in spectral reflectivity is clearly evident (Fig. 2b). This liquid layer persisted throughout Fig. 3 presents profiles of the total reflectivity, the vertical speed of the slowest falling hydrometeor, the linear depolarization from the HSRL, and the liquid water path from the microwave radiometer. Data between UTC and UTC were used for the analysis (~ 8 minutes), during which period the mean wind speed was approximately constant at 5 m s -1. of the hydrometeors. Less common, the precipitation mode separates into two or more branches (e.g., indicated by A, B, and C in Fig. 1e; three ice modes seen in Fig. 4g & 4h). This splitting suggests that there is a sub-section of the population that converts to a different terminal fall velocity class (likely a heavily rimes hydrometeor type,

5 observed during M-PACE; McFarquhar et al., 2007). Figure 2: (a) The sounding at 1059 UTC and 1659 UTC obtained from Barrow, Alaska. (b) Spectrograph at UTC for the Oct 6 th 2004.

6 The slowest falling ice mode in Fig. 2b and Fig. 4g &4h originated in the liquid layer at 1.9 km. The temperature in this liquid layer is -7ºC. Forward Scattering height later in the day. Using only the liquid contribution to the reflectivity as derived from the Doppler spectra, we estimated liquid water contents of 0.1 g m g m -3 and effective radii on the order of 11 micron during this time period, using the algorithms suggested by Shupe et al. (2005). These radar estimated values are consistent with the aircraft observations, lending confidence that this cloud layer contained drops with diameter >23 micron. Looking at individual spectra in the cloud layer, we can see that ice particles with fall speeds of ~ 1.5 m s -1 were present in the cloud (the difference between the velocities at the left and right edges of any spectrum in Fig. 4d is an estimate of the fastest falling hydrometeor speed). Thus, all the necessary conditions for secondary production of ice via ice splintering during riming (Hallett and Mossop, 1974) were met in this layer. This conclusion is consistent with that of Rangno and Hobbs (2001) who suggested that the conditions for ice splintering were met in localized pockets in the liquid layers. Nucleation rates via rime splintering will dominate new ice formation via heterogeneous primary ice production from ice nuclei, the rate of which are low at this temperature (Pruppacher and Klett, 1997). Comparing the two precipitating ice modes in Fig. 2b one can see that the difference in the mean velocity of each mode decreases with distance below cloud base. This decrease may be explained by examining the effect of vapor depositional growth on the fall speed of the two Spectrometer Probe mean diameters on the order of 20 micron were measured in a liquid layer at approximately the same populations. The fall velocity of smaller ice particle increases faster with diameter than a larger ice particle because the asymptotic dependence of the terminal fall velocity on diameter for the pristine/aggregate ice classes. One then concludes that the subcloud is saturated with respect to ice. The HSRL indicates the presence of a thin liquid layer at 1.5 km early in the analysis period (Fig. 3c). The radar spectra revealed no evidence of a separate cloud mode at this altitude. This failure to detect the cloud mode may be explained by the presence of small drops in this layer and/or the shallowness of the cloud, which if less than the range gate size (45 m) will further reduce the returned power to a level below the noise level. However, a sharp increase in the reflectivity in both ice precipitation modes at this level (Fig. 2b) is evidence of riming, and thus indirect evidence of the liquid. Moreover, the expanded time window afforded by Fig. 4f 4h shows the impact of the liquid layer on the precipitation modes. One can see a clear increase in spectral reflectivity in both precipitation modes going from 4f to 4g, and also the development of faster falling modes (4g & 4h). 5. DISCUSSION AND CONCLUSIONS The analysis of Doppler velocity spectra presented here documented microphysical processes in a deep precipitating cloud system observed during the Mixed-Phase Arctic Cloud Experiment over the North Slope of Alaska. This cloud system consisted of ice precipitating out of a thin

7 Figure 3: (a) Reflectivity plot between times and UTC up to a height of 4 km, (b) Slowest hydrometeor fall velocity plotted between same times and height, (c) HSRL linear depolarization (log10[percent depolarization]) for the same time period, (d) shows the liquid water path obtained from the Microwave Radiometer at Barrow for the same time period.

8 Velocity (m/s) Figure 4: (a)-(h) Are times series plots of velocity at the heights indicated at bottom left. The pink arrow with text indicates the time at which the spectrograph in Fig. 1(b) was obtained. The times series plots have the same colorbar as in the spectrograph shown in Fig. 2(b).

9 liquid layer at 4 km, falling through multiple layers of liquid cloud. The spectral analysis revealed the presence of these weakly reflecting liquid layers even in the presence of highly reflective ice precipitation. The formation of new ice through rime splintering was document in one of these imbedded liquid layers. The evolution of different ice modes suggests that much of the 4 km thick layer was at or above ice saturation. With most of the 4 km layer characterized by strong static stability, and perturbed by radiatively driven convection in the top-most liquid layer, much of the middle levels are strongly perturbed by gravity waves. We speculate that the intermittent clouds at km (Fig. 3b, 4a, 4b; 13:22 UTC UTC) are formed when pockets of higher saturation experience upward forcing by these gravity waves. Further evidence for this can be seen in Fig. 3b where a maximum in upward velocity in the imbedded cloud layer is present during that period. Coincident with this vertical velocity maximum, one sees a thickening of the liquid layer in the lidar depolarization, and an increase in the total reflectivity. Interestingly, the thin a liquid layer detected by the lidar at 1.5 km, but with reflectivity below the minimum radar sensitivity, had the strongest impact on the precipitating ice. Interaction between this thin cloud and the precipitating ice resulted in a sharp increase in reflectivity and a newly formed class of hydrometeor, characterized by faster fall speeds (Fig. 4h). Acknowledgments. This research was supported by the Office of Biological and Environmental Research of the U.S. Department of Energy as part of the Atmospheric Radiation Measurement Program. REFERENCES Babb, D.M., J. Verlinde, and B. A. Albrecht 1999: Retrieval of Cloud Microphysical Parameters from 94-GHz Radar Doppler Power Spectra. J. Atmos. Oceanic Technol. Sci., 16, Curry, J.A., and others, 2000: FIRE Arctic Clouds Experiment. Bull. Amer. Meteorol. Soc., 81, Curry, J.A., J.O. Pinto, T. Benner, and M. Tschudi, 1997: Evolution of the cloudy boundary layer during the autumnal freezing of the Beaufort Sea. J. Geophys. Res., 102, Pinto, J.O., J.A. Curry, and J. Intrieri, 2001: Cloud-aerosol interactions during autumn over the Beaufort Sea. J. Geophys. Res., 106, Gregory, D. and D. Morris, 1996: The sensitivity of climate simulations to the specification of mixed phase clouds. Climate Dyn., 12, Gossard, E.E., J. B. Snider, E. E. Clothiaux, B. Martner J.S. Gibson, and R. A. Kropfli, and A. S. Frisch 1997: The Potential of 8- mm Radars for Remotely Sensing Cloud Drop Size Distributions. J. Atmos. Oceanic Technol. Sci., 14, Hallett, J. and S. C. Mossop, 1974: Production of secondary ice particles during the riming process. Nature, 249, Harrington. J. Y., T. Reisin, W.R. Cotton, and S. M. Kreidenweis 1999: Cloud resolving simulations of Arctic stratus Part II: Transition-season clouds, Atmos. Res., 51, Hobbs, P. V. and A. L. Rangno 1998: Microstructures of Low and Middle-Level Clouds over the Beaufort Sea. Q. J. Roy. Meteor. Soc., 124, Intrieri, J. M., M. D. Shupe, T. Uttal, and B.J. McCarty 2002: An annual cycle of Arctic cloud characteristics observed by radar and lidar at SHEBA. J. Geophys. Res., 107, SHE5. McFarquhar, G. M., G. Zhang, M. R. Poellot, G. L. Kok, R. McCoy, T. Tooman, A. Fridlind, and A. J. Heymsfield 2007: Ice properties of single-layer stratocumulus during the Mixed-Phase Arctic Cloud Experiment: 1. Observations

10 . J. Geophys. Res., 112, D Persson, P. O. G., C. W. Fairall, E. L. Andreas, P. S. Guest, and D. K. Perovich, 2002: Measurements near the Atmospheric Surface Flux Group tower at SHEBA: Near-surface conditions and surface energy budget, J. Geophys. Res., 107, C10. Pinto, J. O., 1998: Autumnal mixed-phase cloudy boundary layers in the Arctic. J. Atmos. Sci., 55, Pruppacher, H.R., and J.D. Klett, 1997: Microphysics of Clouds and Precipitation. 976 p. Rangno, A.L., and P.V. Hobbs, 2001: Ice particles in stratiform clouds in the Arctic and possible mechanisms for the production of high ice concentrations. J. Geophys. Res., 106, Shupe, M.D., T. Uttal, and S.Y. Matrosov, 2005: Arctic cloud microphysics retrievals from surface-based remote sensors at SHEBA. J. Appl. Meteor., 44, Shupe, M. D., and S.Y. Matrosov, 2006: Arctic mixed-phase cloud properties derived from surface-based sensors at SHEBA. Atmos. Sci., 63, Shupe, M. D., P. Kollias, S. Y. Matrosov, and T. L. Schneider, 2004: Deriving Mixed-Phase Cloud Properties from Doppler Radar Spectra. Atmos. Oceanic Technol Sci., 21, Sun, Z,. and K.P. Shine 1994: Studies of the radiative properties of ice and mixedphase clouds. Quart..J. Roy. Meteorol. Soc., 120, pp Verlinde, J., and others, 2007: The mixedphase Arctic cloud experiment. Bull. Amer. Meteor. Soc., 88, Witte, H.J., 1968: Airborne observations of cloud particles and infrared flux density in the Arctic. M.S. thesis, University of Washington, Seattle. Yannuzzi, V.T., 2007: A Statistical Comparison of Forecasting Models across the North Slope of Alaska during the Mixed-Phase Arctic Cloud Experiment. M.S. thesis, Penn State University, UP.