Black Carbon in Snow:Treatment and Results

Mark Flanner1

Charlie Zender2

Jim Randerson2

Phil Rasch1

1 NCAR2 University of California at Irvine

2

Motivation

Hansen and Nazarenko (2004) Soot climate forcing via snow and ice albedo, PNAS.

3

The SNICAR Model

Replaces existing snow albedo and heating representation in CLM

Applies a two-stream, multi-layer radiative transfer model (Toon et al., 1989) to predict fluxes with any air/ice/aerosol mixtures.

Mie scattering solutions predicted offline for ice and aerosols

Assumes internally and externally-mixed BC Uses 5 spectral bands and vertical layers that

match CLM thermal snow layers BC (2 species) deposits from atmosphere

(prognostic aerosol model), influences radiation, and flushes through snow column with meltwater

Prognoses effective grain size with a microphysical model, parameterized for GCM

4

The importance of snow aging

Snow exhibits large variability in grain size (30 < re < 2000 μm)

Snow effective grain size determines: Pure-snow reflectance Depth-profile of solar absorption Magnitude of perturbation by impurities

Albedo perturbation caused by a given mass of BC varies more than three-fold for a reasonable range of effective grain size.

Microphysical model predicts snow specific surface area (effective radius) from diffusive vapor flux amongst grains, depending on: snow T, dT/dz, density, and initial size distribution (Flanner and Zender, 2006, JGR).

5

Aerosol induced snow heating:multiple positive feedbacks

Snow/IceCover

Albedo

R_net

(-)

(+)

(-)+

SnowGrainSize

(-)(+)

+ G

Soot(-)

(+)

+

(+) ?

Concentration of hydrophobic and large impurities at the surface during melting?

(+)

6

Measured and modeled BC in snow

Flanner et al. (2007) Present day climate forcing and response from black carbon in snow, J. Geophys. Res.

7

Radiative forcing pattern of BC in snow

Forcing operates mostly in local springtime, when and where there is large snow cover exposed to intense insolation, coincidentally with peak snowmelt. Hence, it is a strong trigger of snow-albedo feedback, which is maximal in spring (Hall and Qu, 2006).

Forcing is dominated by FF+BF sources, but strong biomass burning events can have significant impact on Arctic

8

Global mean forcing and temperature response

Experiment Forcing (W m-2) ∆Ts Efficacy1998: +0.054 (0.007-0.13) +0.15 4.52001: +0.049 (0.007-0.12) +0.10 3.3FF+BF only: +0.04310x 1998: +0.28 +0.44 3.1

Hansen, et al. (2005) The efficacy of climate forcings, J. Geophys. Res.

9

Climate response

Earlier snowmelt Reduced surface albedo Surface air warming

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Driver of springtime snow cover changeCase PI1: Full pre-industrial equilibrium conditionsCase PI2: PI1 with 380 ppm CO2

Case PI3: PI1 with present-day FF+BF BC+OC active in the atmosphereCase PI4: PI1 with present-day FF+BF BC active in snowCase PI5: PI1 with present-day FF+BF BC+OC active in atmosphere and snowCase PI6: PI5 with 380 ppm CO2

11

Conclusions

Snow microphysical model (SSA evolution) could be useful for other CHEM studies e.g., “bromine explosion”

Springtime snowpack is highly sensitive to reflectance changes

Snow-albedo and microphysical feedbacks amplify initial (small) radiative forcing from BC, producing greater “efficacy” than any other forcing agent

Future: Examine radiative effects of dust (Zender), OC (new, absorptive optical properties), algae (?)