Accounting for the layering of snow and firn
On the link between density and grain size variability
M.W. Hörhold*, S. Linow**, J. Freitag**
* IUP, Bremen University,
** Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research
measurements parametrization (Linow et al., 2012)
parametrization
(this study)
result density residual
density residual grain size annual mean
temperature, accumulation rate, ∆T
temperature and grain size profile
mean
grain size
absolute grain size
B36 - density
20 40 60 80 100
depth (m)
density (g/cm
B36 - grain size
0 5 10
depth (m)
grain radius (mm)
0.0 0.2 0.4 0.6 0.8 1.0
0.0 0.2 0.4 0.6 0.8 1.0
mean
variability random
AMSR-E ascending / SSM/I AMSR-E descending
220 200 180 160 140 120 100
220 200 180 160 140 120 100
0 2 4 6 8 10 12
depth (m)
residual specific surface (1/cm)
0 5 10 15
-5
-10
residual specific surface (1/cm)
0 5 10 15
-5 -10 -15
-20-0.2 -0.1 0.0 0.1
residual density (g/cm3)
Figure 3: Measured (red dots, CT) and modelled (blue line, from high-resolution density data) residual specific surface over depth
frequency (GHz)
bias (K)
mean random variability
6 10 18 19 36 37
frequency (GHz)
6 10 18 19 36 37
■ strong linear relationship
between residual (detrended) specific surface and residual density of the analyzed firn cores
Parametrization of grain size variability
■ density variability in polar firn is connected to grain size variability via the snow metamorphism process
■ we use density and grain size from CT measurements from 5 antarctic sites to parametrize grain size
variability as a function of density variability
■ this allows us to reconstruct grain size using
1. modeled mean grain size based on annual mean temperature and accumulation rate
2. grain size variability derived from density measurements
Introduction
■ microwave (MW) interaction with dry polar firn is
influenced by the variability of firn density and grain size
■ due to the integration of MW measurements over firn depths of several meters, the effect of layering can be significant
■ in the retrieval of geophysical parameters from MW data in the polar regions, the variability due to layering is
often unconsidered or treated as a stochastic process
■ in this study, we examine the connection between density and grain size variability to improve the representation of firn layering and examine the impact on the modeled
MW signal
Sensitivity of the MW signal to variability
■ we examine the influence of microstructure variability on the MW signal under different assumptions:
1. mean profile
2. mean profile + random noise 3. parametrization of variability
■ we use microwave data from AMSR-E and SSM/I to
analyse the influence of layering on the microwave signal
■ models: MEMLS and DMRT/ML
■ bias = [Σ(TB,modeled -TB,satellite)2 /n]0.5 depends on the profile type, for DMRT-ML also on frequency
The B36 test site
■ B36 is a firn core drilled to ~80m
depth at Kohnen Station, Antarctica
■ the mean annual temperature
is -44.6˚C, the accumulation rate is 0.065m w.e./year
Results ( B36 )
■ MEMLS:
Conclusions
■ we can show that grain size variability is coupled to density variability, and are able to reconstruct grain size from measured density profiles
■ first sensitivity studies with MW models show
improved results when a realistic variability is used
■ future studies will extend this analysis
■ DMRT-ML:
■ reconstructed specific surface variability based on density
measurements
Figure 1: Measured grain size (Computer Tomography) and measured high-resolution density (Gamma-absorption)
Figure 2: Measured residual specific surface vs. measured residual density (Computer Tomography) of 5 antarctic sites with regression line, correlation coefficient = 0.886
Figure 5: Measured brightness temperatures of different channels compared to model runs (MEMLS)
Figure 6: Measured brightness temperatures of different channels compared to model runs (DMRT-ML)
Figure 7: Comparison of the bias between modelled and measured brightness temperatures for different firn profile types
