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 1987-2001 mean
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)
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)
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
Surface-based Instrumentation: May 1-8 time series
8
-45
-20
-5
dBZ
6
35 GHz cloud radar
ice cloud properties
km
4
2
depolarization lidar-determined liquid cloud base
Microwave radiometer-derived liquid water paths
100
g/m^2
1
1
2
3
4 day
6
7
day 5
4X daily soundings. Near-surface T ~ -20 C, inversion T ~-10 C
lidar cloud base
4
8
8
z
SURFACE INSTRUMENTATION
PURPOSE
HOW
ICE
cloud radar
(35 GHz f, 8.6 mm l)
Ice phase properties
Matrosov et al. (2002,2003)
LIQUID (adiabatic characterization)
depolarization lidar
(0.5235 mm l)
liquid water cloud base Intrieri et al. (2002)
4X daily soundings
T, RH
microwave radiometer
(23.8 & 31.8 GHz f)
liquid water path
ISSUES:
Yong Han physical retrieval
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.
LIQUID FIRST
Liquid/ice discrimination
based on:
• depolarization ratio value
• backscattered intensity
gradient
Depolarization ratio
ice
water
Nov
May 6. Intrieri et al., 2002
Aug
Monthly-averaged percentages of
Vertical columns containing
liquid (grey bars)
adiabatic calculation
constrained by……
Frequency (GHZ)
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
wavelength
Clough et al., ‘89
May 4 & 7 NCAR C130
Research Flights
instrument
• FSSP-100
range (micron)
2-47
• 1D OAP-260X (May 4)
40-640
• 2D OAP (May 7)
25-800
• Cloud Particle Imager
5-2000
• King hot-wire probe
parameter
liquid, ice size distribution
ice size distribution
ice shape, size
particle phase, shape, size
liquid water content
establishes liquid droplet concentration and distribution width
Liquid Characterization
May 4
Cloud radar reflectivity
dBZ
0
2
Height (km)
-50
-50
1
Temperature inversion
Aircraft path
Lidar cloud base
22:00
UTC
23:00
time
24:00
Liquid Water Content: Adiabatic
Ascent Calculation
• lidar-determined liquid
cloud base parcel
• interpolated sounding
temperature structure
• constrained w/ microwave
radiometer-derived liquid
water path
excellent correspondence
between adiabatic calc. and
King probe LWC
May 4
adiabatic LWC
1.0
King LWC
CB
0.6
Z
(km)
0
0.5
Liquid water content g/m^3
Derivation of liquid volume extinction
coefficient b and effective particle radius re
• Lognormal droplet size
distribution
<rk> = <rok>exp(k2s2/2) (Frisch et
May 4
re
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)
adiabatic
aircraft
b
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, re, t time series
b
0
30
re
0
re
1
2
3
4 day5day 6
7
8
30
t
0
Mean liquid cloud optical depth ~ 10, mean r_e ~4.5
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
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
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)
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
dBZ
radar
liquid
• Radar retrievals agree
with aircraft-derived
values given large
uncertainties (~4x ?)
• Ice cloud optical depth
almost insignificant
b
Ice aircraft
IWC
Deff
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. 2004
• 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, 1998.
Surface-based, mixed-layer, mixed-phase cloud
Overlaps with the first two FIRE.ACE* flights
*Arctic 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
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
Most common ice particle habit: aggregate
number
area
mass
aggregates, small&big
spheres
(below liquid cloud base)
Comparison of modeled to observed surface
downwelling radiative fluxes, May 1 -8
shortwave
observed
longwave
modeled W m-2
• 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)
How do clouds impact the surface ?
Jnoon = 60o
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 !
Longwave
For cloud optical depth<3, net cloud
forcing dominated by longwave
=> Sensitive to optical depth
changes
For cloud optical depth > 6, net
cloud forcing dominated by
shortwave
=> Sensitive to solar zenith angle,
surface reflectance changes
Shortwave
Net
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)
+5
+20
-5
-20
D CF (W m-2)
+2
+3
-3.5
-10
D surface D CF (W m-2)
+0.05
-0.05
+4.5
-3.8
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)
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
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
http://www.etl.noaa.gov/~pzuidema
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
University of Colorado/
NOAA Environmental Technology Laboratory, Boulder, CO
Temperature inversion
1.0 km
Aircraft path
Cloud radar reflectivity
Lidar cloud base
time
Height (km)
Paquita Zuidema
Liquid phase top agrees well with the
location of the temperature inversion
2 km
Cloud radar top
1 km
Temperature inversion
1
2
3
4 day 5
6
7
8
9
10
Aircraft-adiabatic calc. optical depth
comparison
with microwave,
agreement to 10%
w/out microwave,
agreement to
a factor of 2
tadiabatic
Uses microwave LWP
taircraft
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)