Investigating Antarctic ice sheet temperature profile by using microwave low-frequency measurements Microsnow2 – Columbia, MD, July Marco Brogioni 1, G. Macelloni 1, C. Yardim 2, J. T. Johnson 2, M. Aksoy 2, A. Bringer 3, K. C. Jezek 4, M. Durand 4, Y. Duan 4, S. Tan 5, T. Wang 5, L. Tsang 5, M. Drinkwater 6 1 Institute of Applied Physics, Florence, Italy, 2 Electro Science Laboratory and 3 Byrd Polar Research Center, The Ohio State University, Columbus, OH, 4 Department of Electrical Engineering, University of Michigan, 5 ESA-ESTEC

Introduction The importance of ice sheets for the Earth climate is clear and recognized. In particular, understanding dynamics of Earth’s ice sheets are important for future prediction of ice coverage and sea level rise Extensive past studies have developed a variety of sensing techniques for ice sheet properties (e.g. thickness, topography, velocity, mass, accumulation rate) using EO data. Nevertheless some parameters are poorly known or present high incertitude (e.g. the internal temperature profile which influences stiffness, which influences stress-strain relationship and therefore ice deformation and motion) Low-frequency passive microwave sensors are able to penetrate very deep in the ice. Can they provide useful information?

Low microwave freq & Ice Sheets The availability of L-band spaceborne radiometers data from SMOS ( Aquarius, and SMAP) stimulate the investigation on new research topics beyond soil moisture and ocean salinity Also the Tb of the Antarctic and Greenland Ice sheet, which was initially observed for calibration purposes, was recently investigated since it presents some interesting Tb signatures that need to be better interpreted

SMOS – Internal East Antarctic Plateau V – polarization – theta = 25°H – polarization – theta = 25° Lake Vostok Triangle – near Dome-C site – (No such big lake on the bottom !) L-band mw penetrate about 500m in ice these features are not related to the bedrock but to the overlying ice structure

Spatial Analysis : L-band Transects In order to investigate on ice sheet geophysical properties two trasects were analyzed: Transect 1: From DomeC to the triangle (400 km) Transect 2: From DomeC to Lake Vostok (800 km) Dome -C Lake Vostok

Ancillary data: Surf. Temperature & Bedrock Modis data- MOD11C3V41 Bedmap-2 Project Mean Surface Temperature ( ) Ice Thickness

Ice Thickness Surface Temperature Transect-2 Transect -1 : DomeC area SMOS Ice thick SMOS Surf. T Ice Thickness Increases Surf. Temp. Constant Surface Temperature Increases Ice Thickness constant

Ice Thickness Surface Temperature Transect -2 : DomeC - Vostok Lake Vostok Ice Thickness Increases Surf. Temp. Constant Surface Temperature Increases Ice Thickness decreases SMOS Ice thick SMOS Surf. T

Greenland Ice Sheet SMOS data Also in Greenland Tb features can be recognised

SMOS vs Ice Sheet Properties costal zone Along the coast Tb is influenced by others factors costal zone costal zone costal zone plateau Some corr. with snow accum.

Are we able to simulate the SMOS data ?  e.m. model We use DMRT-ML which was developed under the Quasi-Crystalline Approximation with Coherent Potential - QCACP (implemented at LGGE). It simulates the snowpack emission under the Rayleigh approximation. Model is validated using L-and C-band data acquired at Concordia station (i.e. where ancillary data are available) Extension to the plateau is performed to explain spatial variability Model Inputs: - Snowpack temperature profile -Snowpack density profile -Snowpack grain radius profile -Layers profile SMOS data interpretation :Model analysis

From e.m. point of view the ice sheet could be represented as a multi-layered medium. Each layer is Characterized by : Thickness Density Temperature Grains Size (at the moment only horizontal flat interfaces are considered) Upper layer of ice sheet comprised of snow: high volume fraction of ice crystals in air Medium typically represented as air containing spherical ice particles Particle radius typically characterized by the “grain size” parameter “Dense medium” from electromagnetic point of view Ice Sheet Properties l Density on average increases with depth –Volume fraction of ice increases and passes 50% at ~ 20 m depth l Medium is now air inhomogeneities in ice background l Inhomogeneity volume fraction on average decreases with depth past this point –Grain size increases with depth (even if at L-band volume scattering is negligible) l Medium on average approaches homogeneous ice at depths ~ 100 m l “Random” variations in density and composition with depth on top of the average trends can appear as “layering” effects

Model inputs : snow temperture profile T mean annual value T surface (July) T surface (January) m : seasonal temperature swing (Bingham and Drinkwater, 2000). Tm = mean annual value ; Ta = seasonal T amplitude. below 10 m : (Jezek et al. 2013)

The density profile is modeled as the continuous profile is (Alley, 1982 for coarse grains as they found in Dome C) the noise N() has a Gaussian distribution with independent realizations. The standard deviation  ρ and the damping factor  ρ are (Brogioni et al., 2015) Obtained by fitting Tb measurements at L- and C- band at Dome C (Macelloni et al., 2006, Macelloni et al., 2013) Model inputs : density profile

In the first 50 m the agreement between model and experimental data is very good Below ~75m the density increases much rapidly due to well known microscale processes (Horhold et al., 2011) Model inputs : density profile

Horhold et al.,2011 Our model (one realization) Urbini et al.,2011 Model inputs : density profile

Model inputs : grain radius The grain size profile was obtained from (Zwally, 1977) adjusted to fit the Tb at Brewster angle k optimum =3.7 The influence of the grain radius on the emission Fluctuation of a does not affect emission

By using the mean density profile and a constant surface accumulation rate the mass continuity can be expressed as where (h(j), ρ(j)) and (h(j+1), ρ(j+1)) are the thickness and density of two overlapped layers. h(0) was assumed to be equal to one year’s worth of accumulation which, for Dome-C area is about 0.1 m/yr. To model the layer thickness in the first 50 meters (i.e. the depth where the density fluctuations reach the minimum as shown before) we use the densification approach. Model inputs : layering 100 to 300m  400 layers 50cm each 300 to 3200m  480 layers 6 m each Below 3200  water – half space Deeper layers

Model simulations bedrock Snow temp. surface Tb

Model simulations Modification of Surface Temperature

Good Agreement with Data Model validation: Ground based Radiometer acquired at Concordia L-C band Brogioni et. al, IEEE JSTAR

Model and Data Comparison: Transect 1 Model SMOS data Ts Ice Thick. Ice Thickness Increases Surface Temperature Increases Geophysical ParametersL-Band Model & Data

Model and Data Comparison:Transect 2 Model SMOS data Ts Ice Thick. Geophysical ParametersL-Band Model & Data

Simulation of SMOS in Greenland 500 MHz 1 GHz 1.4 GHz 2 GHz SMOS

Current intiatives on : Low Frequency & Ice sheets CRYOSMOS funded by ESA as Support To Science Elements (STSE), where cases study will be there analyzed in order to better understand the Tb signature and provides new potential SMOS derived products: -Quantifying internal ice-sheet temperature -Study bedrock topography and/or geothermal heat flux -Characterization of surface processes -Characterization of ice shelves (for further detail see Marion Leduc-Leballeur poster on Tue at 4:00 PM) UWBRAD – founded by NASA – (ESTO) which propose a multi-frequency microwave radiometer operating in the 0.5 – 2 GHz range for internal ice sheet temperature sensing (for further detail see Mike Durand presentation on Tue at 10:00 AM)

Summary The analysis of SMOS data over Antarctica pointed out a sensitivity to geophysical properties of the ice sheet (shelves, wet, inner part). These properties will be investigate in a near future. Preliminary investigation demonstrates that the Tb spatial features were observed in the Antarctic plateau could be explained by using an e.m. model and ancillary data. It was demonstrated that L-band is sensitive to snow temperature profile which in turn is dominated by ice sheet geophysical parameters. In particular surface temperature, ice thickness and accumulation are the key parameters for the temperature profile. Obtained results open to the possibility of developing new algorithms and methodologies for the retrieval of ice sheet properties  Is SMOS a cryosphere mission ? New opportunities for future activities and/or missions !

Thanks for the attention! Greetings from Concordia Station

Ultra-wideband software defined radiometer (UWBRAD) We propose design of a radiometer operating 0.5 – 2 GHz for internal ice sheet temperature sensing Requires operating in unprotected bands, so interference a major concern Address by sampling entire bandwidth (15x100 MHz channels) and implement real-time detection/mitigation/use of unoccupied spectrum Supported under NASA 2013 Instrument Incubator Program Goal: deploy in Greenland in 2016 Retrieve internal ice sheet temperatures and compare with in-situ core sites

“Database” of 1585 differing brightness temperatures vs. frequency created using DMRT-ML simulator A selected truth case perturbed with ~ 1 K NEDT noise on each frequency channel and “closest” profile from database selected Retrieval of Temperature using UWBRAD

100 Monte Carlo trials for each truth case showed ~74% of correct l Continuing to include “random” layering and coherency effects, expand range of cases simulated, and develop UWBRAD temperature retrieval algorithms Initial UWBRAD Retrieval Studies Retrieved temperature RMS Error (K) Number of truth cases (out of 1585) Percent Classified CorrectlyDepth (m)

Initial Retrieval Studies for Greenland o Antarctica :  Cold Surface Temperatures  Low accumulation rates  Strong changes in TB vs. frequency o Greenland :  Warmer Surface Temperatures  Higher accumulation rates in Greenland  Smaller changes in TB vs. frequency  Still observable by UWBRAD Antarctica Greenland (GISP) Blue: With Antenna Red: Without Antenna Blue: Simulated Profiles Red: GISP Data

Greenland Retrieval Studies Generated simulated UWBRAD observations “GISP-like” ice sheets for varying physical properties (500 “truth” cases)  Including averaging over density fluctuations For each truth case, generate 100 simulated retrievals with UWBRAD expected noise levels (i.e. ~ 1 K measurement noise per ~ 100 MHz bandwidth) Select profile “closest” to simulated data as the retrieved profile, and examine temperature retrieval error Errors in this simulation meet science requirements Additional simulations continuing over Greenland flight path