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Characterization and Radiative Impact of a Springtime Arctic Mixed-Phase Cloudy Boundary

Characterization and Radiative Impact of a Springtime Arctic Mixed-Phase Cloudy Boundary Layer observed during SHEBA. Paquita Zuidema. University of Colorado/ NOAA Environmental Technology Laboratory, Boulder, CO. Surface Heat Budget of the Arctic. SHEBA. Early May ~ 76N, 165 W.

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Characterization and Radiative Impact of a Springtime Arctic Mixed-Phase Cloudy Boundary

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  1. Characterization and Radiative Impact of a Springtime Arctic Mixed-Phase Cloudy Boundary Layer observed during SHEBA Paquita Zuidema University of Colorado/ NOAA Environmental Technology Laboratory, Boulder, CO

  2. Surface Heat Budget of the Arctic SHEBA

  3. Early May ~ 76N, 165 W

  4. WHY ? • GCMs indicate Arctic highly responsive to increasing greenhouse gases (e.g. IPCC) • Clouds strongly influence the arctic surface and atmosphere, primarily through radiative interactions • Factors controlling arctic cloudiness not well known Observational evidence may support predictions: (Serreze et al. 2000)

  5. Arctic Sea Ice Extent in 2002 strongly diminished relative to 1987-2001 mean

  6. Annual warming dominated by winter and spring spring warming ~ 0.5 C/decade in SHEBA region Spring 1966-1995 Temperature Trends (Serreze et al., 2000; Jones 1994)

  7. spring Increased Spring And Summer Cloudiness summer 1982-1999 AVHRR data (Wang&Key, 2003) annual Persistent springtime cloud cover may advance snowmelt onset date (e.g., modeling study of Zhang 1996)

  8. Project Goal • characterize a multi-day arctic cloud sequence as best possible • elucidate the underlying cloud physical processes • assess the cloud’s radiative impact. The Case: May 1- May 10, 1998. Surface-based, mixed-layer, mixed-phase cloud Overlaps with the first two FIRE.ACE* flights *Arctic Clouds Experiment The challenge: both ice and liquid phases are present

  9. Surface-based Instrumentation: May 1-8 time series 8 dBZ -20 -45 -5 6 35 GHzcloudradar ice cloud properties km 4 2 depolarization lidar-determined liquid cloud base Microwave radiometer-derived liquid water paths 100 g/m^2 2 3 4 5 6 7 8 1 day day 4X daily soundings.Near-surface T ~ -20 C, inversion T ~-10 C 1 4 lidar cloud base 8 z -30C -10C

  10. SURFACE INSTRUMENTATION PURPOSE HOW cloud radar (35 GHz f, 8.6 mm l) Ice phase properties Matrosov et al. (2002,2003) depolarization lidar liquid water cloud base Intrieri et al. (2002) (0.5235 mm l) 4X daily soundings T, RH microwave radiometer (23.8 & 31.8 GHz f) liquid water path Yong Han physical retrieval ICE LIQUID (adiabatic characterization) ISSUES: 1) Radar-based estimates of ice cloud properties applied to estimate ice component within mixed-phase conditions, requires validation 2) Adiabatic characterization of liquid phase, requires additional info.

  11. LIQUID FIRST • Liquid/ice discrimination • based on: • depolarization ratio value • backscattered intensity • gradient Depolarization ratio ice water Aug Nov Monthly-averaged percentages of Vertical columns containing liquid (grey bars) May 6. Intrieri et al., 2002

  12. Frequency (GHZ) wavelength Clough et al., ‘89 adiabatic calculation constrained by…… Microwave-radiometer-derived Liquid water paths: • microwave radiometer responds • to integrated water vapor and • liquid water • physical retrieval also utilizes: • - cloud temperature • - soundings • decreased uncertainty • (good for Arctic conditions) • Yong Han, unpublished data

  13. May 4 & 7 NCAR C130 Research Flights range (micron) instrument parameter • FSSP-100 2-47 liquid, ice size distribution • 1D OAP-260X (May 4) 40-640 ice size distribution • 2D OAP (May 7) 25-800 ice shape, size • Cloud Particle Imager 5-2000 particle phase, shape, size • King hot-wire probe liquid water content establishes liquid droplet concentration and distribution width

  14. Liquid Characterization May 4 Cloud radar reflectivity dBZ -50 0 -50 2 Height (km) 1 Temperature inversion Aircraft path Lidar cloud base UTC 24:00 22:00 23:00 time

  15. Liquid Water Content: Adiabatic Ascent Calculation May 4 • lidar-determined liquid cloud base parcel • interpolated sounding temperature structure • constrained w/ microwave radiometer-derived liquid water path adiabatic LWC 1.0 King LWC CB 0.6 Z (km) excellent correspondence between adiabatic calc. and King probe LWC 0 0.5 Liquid water content g/m^3

  16. Derivation of liquid volume extinction coefficient b and effective particle radius re May 4 • Lognormal droplet size distribution <rk> = <rok>exp(k2s2/2) (Frisch et al., ’95,’98,’02) • mean aircraft cloud droplet conc. N=222 (14) • mean aircraft lognormal of geometric standard deviation of droplet size distribution s =0.242 (0.04) re b adiabatic aircraft

  17. May 7: thin cloud, low LWP Lidar depolarization ratio High aerosols ! Max of 1645/L (Rogers et al., 2001) Backscattered intensity

  18. May 1 – 10 liquid b, re, t time series b 0 30 re 0 re 1 2 3 4 5 6 7 8 day day 30 t 0 Mean liquid cloud optical depth ~ 10, mean r_e ~4.5

  19. May 1-3 Mean Sea Level Pressure Weak low N/NW of ship followed by weak/broad high moving from SW to NE Data courtesy of NOAA Climate Diagnostics Center May 4-9 Mean Sea Level Pressure Boundary-layer depth synchronizes w/ large-scale subsidence

  20. ICE microphysics retrieval • radar only (Matrosov et al. 2002; 2003) • particle size retrieved from Doppler velocity • particle mass retrieved from reflectivity & • particle size MAY 5

  21. ISSUES: • Radar retrieval developed for ice clouds, not ice+liquid clouds • Radar not sensitive to the smaller particles • Another degree of freedom: Particle shape • for bulk aircraft measurements, complete size distributions difficult to form Comparison to aircraft data uncertain IWC comparison most reliable (not D or b)

  22. May 4 Cloud Particle Imager data …pristine ice particles from upper cloud ...super-cooled drizzle

  23. May 4 complete size distribution: FSSP (*), CPI (line), 260X (triangles)

  24. Robust conclusions: dBZ b radar • Radar reflectivity insensitive to liquid when ice is present • Radar retrievals agree with aircraft-derived values given large uncertainties (~4x ?) • Ice cloud optical depth almost insignificant liquid Ice aircraft Deff IWC

  25. What is the radiative impact of the ice ? • Direct impact negligible: mean ice cloud optical depth ~0.2 BUT: • 1) upper ice cloud sedimentation associated with near-complete or complete LWP dissipation* (May 4 & 6) • 2) local IWC variability associated with smaller LWP changes, time scale ~ few hours * At T=-20C, air saturated wrt water is ~ 20% supersaturated wrt ice

  26. Ice water content/LWP time series

  27. Mechanism for local ice production: • Liquid droplets of diameter > ~ 20 micron freeze preferentially, grow, fall out • New ice particles not produced again until collision-coalescence builds up population of larger drops • Only small population of large drops required • Hobbs and Rangno, 1985; Rangno and Hobbs, 2001; Korolev et al. 2003; Morrison et al. 2004 • Little previous documentation within cloud radar data

  28. Local ice production more evident when boundary layer is deeper and LWPs are higher May 3 counter-example – variable aerosol entrainment ? Quick replenishment of liquid: longer-time-scale variability in cloud optical depth related to boundary layer depth changes

  29. Project Goal • characterize a multi-day arctic cloud sequence as best possible • elucidate the underlying cloud physical processes • assess the cloud’s radiative impact. The Case: May 1- May 10, 1998. Surface-based, mixed-layer, mixed-phase cloud Overlaps with the first two FIRE.ACE* flights *Arctic Clouds Experiment

  30. Radiative flux closure and cloud forcing Implement derived cloud properties within radiative transfer model Streamer (Key & Schweiger; Key 2001). Medium-band code, utilizes DISORT (Stamnes et al. 2000) Strength: comprehensive, adapted for Arctic climate problems • Both phases represented within a single volume • Shortwave ice cloud optical properties parameterized for 7 • particle habits • Arctic aerosol profile available • surface albedo spectral variation adequately represented Weakness: 4 gases only, outdated gaseous line information

  31. Clear-sky comparison (May 7 & April 25) • SHEBA spectral surface albedo data (Perovich et al.) • time-mean broadband albedo = 0.86 • (matches surface-flux albedo) • Arctic haze aerosol profiles constrained with • sunphotometer measurements (R. Stone, unpub. data) • Aerosol optical depth = 0.135 @ 0.6 micron • Ozone column amount = 393 DU (TOMS; J. Pinto pers. comm.) Shortwave and infrared calculated and measured Downwelling surface fluxes agree to within 1 W/m^2

  32. area mass number Most common ice particle habit: aggregate aggregates, small&big spheres (below liquid cloud base)

  33. Comparison of modeled to observed surface downwelling radiative fluxes, May 1 -8 • modeled LW > observed LW by 1 W m-2 ; RMS dev. = 13 W m-2 or 13% of observed fluxes • modeled SW > observed SW by 3 W m-2 ; RMS dev. = 17 W m-2 or 12% of observed fluxes • Bias slightly larger for low LWP cases • Small bias encourages confidence in data (better agreement cannot be achieved w/out exceeding estimated uncertaintities) longwave shortwave observed modeled W m-2

  34. Jnoon = 60o Clouds decrease surface SW by 55 W m-2 ,increase LW by 49 W m-2 How do clouds impact the surface ? Surface albedo=0.86; most SW reflected back Clouds warm the surface, relative to clear skies with same T& T & RH, by time-mean 41 W m-2* (little impact at TOA) • Can warm 1m of ice by 1.8 K/day, or melt 1 cm of 0C ice per day, • barring any other mechanisms !

  35. For cloud optical depth<3, net cloud forcing dominated by longwave => Sensitive to optical depth changes Longwave For cloud optical depth > 6, net cloud forcing dominated by shortwave => Sensitive to solar zenith angle, surface reflectance changes Shortwave ~30% of cloud optical depths < 3 ~60% > 6 Net Cloud optical depth

  36. How sensitive is the surface to cloudiness changes ? • Satellite-based study concludes surface cloud forcing most • sensitive to changes in cloud amount, surface reflectance, cloud • optical depth, cloud top pressure (Pavolonis and Key, 2003) D LWP (g m-2) +5 +20 -5 -20 D CF (W m-2) +2 +3 -3.5 -10 D surface +0.05 -0.05 D CF (W m-2) +4.5 -3.8 • Little radiative impact from additional water • Surface reflectance changes may be more radiatively significant

  37. Why is this cloud so long-lived ???? • Measured ice nuclei concentrations are high (mean = 18/L, with • Maxima of 73/L on May 4 and 1654/L (!) on May 7 (Rogers et al. 2001) • This contradicts modeling studies that find quick depletion w/ IN • conc of 4/L (e.g. Harrington et al. 1999) One part of the explanation: • Cloud-top radiative cooling rates can exceed 65 K/day • Strong enough cooling to maintain cloud for any IN value (Pinto 1998) • Promotes turbulent mixing down to surface, facilitating surface fluxes How did this cloud finally dissipate ???? Strong variability in subsidence rates part of answer

  38. Most interesting results: • Radiative flux impact of this mixed-phase cloud is close to • that of a pure liquid cloud • Two mechanisms by which ice regulates the overall cloud • optical depth: • Sedimentation from upper ice clouds • A local ice production mechanism, though to reflect • the preferred freezing of large liquid droplets • …..but liquid is quickly replenished • Longer-time scale changes in cloud optical depth appear • synoptically-driven • CONCLUSIONS DERIVE THEIR AUTHORITY FROM A COMPREHENSIVE • CHARACTERIZATION OF BOTH LIQUID AND ICE PHASE

  39. What might a future climate change scenario look like at this location ? Recent observations indicate increasing springtime Arctic Cloudiness and possibly in cloud optical depth (Stone et al., 2002, Wang & Key, 2003, Dutton et al., 2003) At this location (76N, 165W) an increase in springtime cloud optical depth may not significantly alter the surface radiation budget, because most cloudy columns are already optically opaque. A change in the surface reflectance may be more influential

  40. Acknowledgements Brad Baker Paul Lawson Yong Han Jeff Key Robert Stone Janet Intrieri Sergey Matrosov Matt Shupe Taneil Uttal Submitted journal article available through http://www.etl.noaa.gov/~pzuidema

  41. Summary & Conclusions • Arctic mixed-phase clouds are common, radiatively and climatically important • Can characterize the liquid with an adiabatic ascent calculation using a saturated air parcel from the lidar-determined liquid cloud base, constrained with the microwave radiometer-derived liquid water path • The ice component can be characterized with cloud radar retrievals, even when LWC is high • This was applied to a May 1-10 time series with some success, judging from comparison to aircraft data and comparison of calculated radiative fluxes to those observed. • For May 1-10: radiative flux behavior is practically that of a pure liquid cloud • The low ice water contents are consistent with what is required for the maintenance of a long-lived super-cooled (~ -20 C) liquid water cloud (e.g., Pinto, 1998, Harrington, 1999) • Usefulness of the technique can be improved even further by improving the microwave radiometer retrievals of liquid water path

  42. Characterization and Radiative Impact of a Springtime Arctic Mixed-Phase Cloudy Boundary Layer observed during SHEBA Paquita Zuidema Height (km) University of Colorado/ NOAA Environmental Technology Laboratory, Boulder, CO Temperature inversion 1.0 km Aircraft path Cloudradarreflectivity Lidar cloud base time

  43. Liquid phase top agrees well with the location of the temperature inversion 2 km Cloud radar top 1 km Temperature inversion day 1 1 2 3 4 5 6 7 8 9 10

  44. Aircraft-adiabatic calc. optical depth comparison with microwave, agreement to 10% w/out microwave, agreement to a factor of 2 Uses microwave LWP tadiabatic taircraft

  45. ICE (radar) • Remote retrieval depends only on cloud radar • Radar-based retrieval developed for all-ice clouds (Matrosov et al. 2002, 2003) extended to mixed-phase conditions, relies only on Z, V. • IWC=Z/(G*D^3) where G assumes exponential size distribution, Brown and Francis bulk density-size distribution • EXT=Z/(X*D^4); X also assumes a mass-area-size relationship for individual particles • Correction accounts for dry air density variation with height • DEFINE D_effective=1.5*IWC/(rA) (Mitchell 2002; Boudala et al. 2002)

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