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

SHEBA Surface Heat Budget of the Arctic

Early May ~ 76N, 165 W

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)

Arctic Sea Ice Extent in 2002 strongly diminished relative to mean

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

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

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, Surface-based, mixed-layer, mixed-phase cloud Overlaps with the first two FIRE.ACE* flights *A rctic Clouds Experiment The challenge: both ice and liquid phases are present

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

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. ISSUES: SURFACE INSTRUMENTATIONPURPOSEHOW cloud radar (35 GHz f, 8.6 mm l)Ice phase properties Matrosov et al. (2002,2003) depolarization lidarliquid water cloud baseIntrieri et al. (2002) ( mm l) 4X daily soundingsT, RH microwave radiometer (23.8 & 31.8 GHz f)liquid water pathYong Han physical retrieval ICE LIQUID (adiabatic characterization)

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

wavelength Frequency (GHZ) Clough et al., ‘89 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 adiabatic calculation constrained by……

May 4 & 7 NCAR C130 Research Flights FSSP liquid, ice size distribution 1D OAP-260X (May 4) ice size distribution 2D OAP (May 7) ice shape, size Cloud Particle Imager particle phase, shape, size King hot-wire probe liquid water content range (micron) parameterinstrument establishes liquid droplet concentration and distribution width

Aircraft path Lidar cloud base Temperature inversion Cloud radar reflectivity time Height (km ) 1 2 dBZ May 4 24:00 22:0023:00 UTC Liquid Characterization

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

Derivation of liquid volume extinction coefficient b and effective particle radius r e Lognormal droplet size distribution = exp(k 2 s 2 /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) May 4 rere b adiabatic aircraft

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

May 1 – 10 liquid b, r e, t time series rere day day t 0 30 Mean liquid cloud optical depth ~ 10, mean r_e ~4.5 b rere

May 1-3 Mean Sea Level Pressure May 4-9 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 Boundary-layer depth synchronizes w/ large-scale subsidence

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

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  ISSUES:

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

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

Robust conclusions: 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 dBZ liquid radar Ice aircraft b IWC D eff

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

Ice water content/LWP time series

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 Little previous documentation within cloud radar data

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

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, Surface-based, mixed-layer, mixed-phase cloud Overlaps with the first two FIRE.ACE* flights *A rctic Clouds Experiment

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

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 = 0.6 micron Ozone column amount = 393 DU (TOMS; J. Pinto pers. comm.) Clear-sky comparison (May 7 & April 25) Shortwave and infrared calculated and measured Downwelling surface fluxes agree to within 1 W/m^2

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

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) modeledW m -2 observed longwave shortwave

How do clouds impact the surface ? J noon = 60 o Clouds decrease surface SW by 55 W m -2,increase LW by 49 W m -2 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 !

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

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 ) D CF (W m -2 ) D surface D CF (W m-2)  Little radiative impact from additional water  Surface reflectance changes may be more radiatively significant

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) 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 One part of the explanation: How did this cloud finally dissipate ???? Strong variability in subsidence rates part of answer

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: 1)Sedimentation from upper ice clouds 2)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

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

Acknowledgements Brad Baker Paul Lawson Yong Han Jeff Key Robert Stone Janet Intrieri Sergey Matrosov Matt Shupe Taneil Uttal Submitted journal article available through

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

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 Aircraft path Lidar cloud base Temperature inversion Cloud radar reflectivity time Height (km) 1.0 km

Liquid phase top agrees well with the location of the temperature inversion Cloud radar top Temperature inversion day 1 km 2 km

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

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)