Estimation of snow/ice parameters
and the effects on climate
Teruo Aoki, Hiroki Motoyoshi, Tomonori Tanikawa,
Masahiro Hori, Knut Stamnes, Taichu Tanaka, Etsuko
Tanaka and Katsuyuki Kuchiki

Contents:
1. Improvements of algorithms and validation method
9Extensions of look-up tables
9Black carbon analysis with EC/OC Instrument
9Retrievals using non-spherical ice particles
2. Snow/ice parameters and the effects on climate
9Physically based snow albedo model
9Radiative effect of aerosol deposition on snow surface

Original algorithm:
9Extents of theoriginal LUTs are notsufficient for both theranges of re and cs.
9When the satellitemeasured reflectanceis outside the LUTs,
the retrieved
parameters areextrapolated.
Improved algorithm:
9Impact of theextension for re wassignificant (-> latterslides).

(cs)

Extensions of look-up tables (LUTs)(re)Feasibility study for black
carbon analysis with
EC/OC Instrument

EC/OC Instrument (Sunset Inc.)

9Thermal/Optical method
9Test result suggests EC(~BC)/OC components could be
successfully measured from clean
and dirty snow samples.
Clear snow (C-sample)
Jan. 3, 2006

Dirty snow with dust (D-sample)
Mar. 13, 2004

Bulk conc.=1.3 ppmw, EC=0.04 ppmw

Bulk conc. = 495 ppmw, EC=2.1 ppmw

Black carbon (soot) analysis

9Test result suggests
EC(~BC) /OC
components could bemeasured from clean
and dirty snowsamples.

(cs)

9Thermal/Opticalmethod

9The extended LUTs
are expected to cover
the actual ranges of cs.
(re)Retrievals of Re and Cs with LUTs
calculated by non-spherical ice particles
9Aspect ratio is fixed to be 5 for non-spherical ice particles
based on the comparison in HDRFs between the calculationand field measurements.
9Grain radius isdefined for the equal
V/A ratio sphere tonon-spherical ice
particle.
Other improvement:
9Two-dimensionalspline interpolation inLUTs
Tanikawa et al. (2006)

Snow grain radius from Chs. at λ = 0.46 and 0.865 µm
R=0.95, RMS=127 µm

R=0.88, RMS=87 µm

R=0.86, RMS=82 µm

R=0.82, RMS=126 µm

R=0.88, RMS=69 µm

R=0.86, RMS=73 µm

OriginalSnow grain radius from Chs. at λ = 1.64 µm
R=0.57, RMS=117 µm

R=0.72, RMS=128 µm

R=0.70, RMS=122 µm

R=0.56, RMS=122 µm

R=0.73, RMS=129 µm

R=0.72, RMS=125 µm

Original

Mass fraction of snow impurities as soot
R=0.12, RMS=4.8ppmw

R=-0.04, RMS=5.0ppmw

R=0.01, RMS=5.0ppmw

R=0.51, RMS=5.0ppmw

R=0.09, RMS=4.4ppmw

R=0.12, RMS=4.6ppmw

OriginalSummary-1
9 Look-up tables were extended to the actually variable
ranges for re and cs as soot.
9 Feasibility study for BC analysis suggested EC(~BC) /OC
components could be measured from clean and dirty
snow samples using EC/OC instrument.
9 Retrievals using non-spherical ice particles for snow grain
size were improved, while the accuracy was not improvedfor snow impurities. -> The detailed analyses are neededusing the validation data of BC concentration (not bulkconcentration).

Snow albedo process in land-surface model of GCM
Present: Snow albedo is parameterized by air temperature, snow
surface temperatures, and elapsed time after snowfall (Empiricalmodel).
↓Snow albedo strongly depends on snow grain size (re) andconcentration of snow impurities (cs), which process is calledsnow aging.
↓
Next generation: re and cs should be treated as predictors inGCM. (Physically-based model)↓
9re should be calculated by heat budget in snowpack
9cs should be calculated from production, transportation, anddeposition of atmospheric aerosols as source for cs.
↑Validation with satellite snow products

Physically-based Snow Albedo Model
DesertMineral dust
MASINGAR(Tanaka and Chiba, 2004,2005)
Aerosol depositions (dry and wet)Black carbon
Albedo: α(re, cBC, cMD, θ0)Mass concentration ofsnow impurities (cBC, cMD)

ARTMASS(Aoki et al, 1999,2000)

Evolution of snow metamorphism
Snow grain size (re)TSnowpack(Leining, 2002)

Black-sky albedo as functions of SIF and reff

Visible albedo

Near-infrared albedo

θ0=63°

SIF

reffθ0=63°SIF

reff

Climate simulation with aerosol transport model
MASINGAR
9Simulation period : Spin up in three months and the calculation
during four years. The latter three years are analysed.
9No data assimilation
9Dry and wet depositions
9Dust and black carbon
9Horizontal resolution: T42 (~2.8°×2.8°)
9Vertical resolution: 30 atmospheric layers (surface ~ 0.8 hPa)
9Calculation condition
yControl run： Depositions of BC and MD
yNo SIF : No depositions of BC and MD
yBC run： Depositions of only BC
yDust run : No depositions of only MD

Tanaka et al. (to be submitted)

Summary-2
9 Quasi‐physically-based snow albedo model depending
on re and c was developed and incorporated into aerosol
transport model MASINGAR.
9 Snow impurity factor (SIF) is introduced to calculate theeffects of BC and dust which have the different lightabsorption.
9 TOA radiative effect of aerosol (BC + dust) depositions onsnow surface was comparable to the aerosol direct orindirect effect.
9 The contribution due to BC is approximately half of thetotal radiative effect.
9 Satellite snow products will be used to validate thesimulation with MASINGAR.