A Multi-Sensor, Multi-Parameter Approach to

Studying Sea Ice: A Case-Study with EOS

DataWalt Meier

2 March 2005 IGOS Cryosphere Theme Workshop

SIMBA

• Sea Ice Mass Balance of the Arctic• NSF organized workshop in Seattle, WA:

28 Feb – 2 Mar, 2005• What are requirements to understand sea

ice mass balance– Data improvements– Model improvements– Find gaps in knowledge and how to fill gaps

• Thickness distribution, snow cover, scaling are key issues

• Possible field camp, submarine cruises in 2006-2007(?)

Satellite Observation of Sea Ice

• Satellites provide a wealth of information on sea ice. 25+ year record:– Passive microwave: extent, concentration,

motion– Visible/Infrared: albedo and temperature

• Information is at different spatial and temporal resolutions and is often difficult to combine

• New suite of EOS sensors provide opportunity to obtain better and more integrated observations

NASA EOS Sensors for the Cryosphere

• Advanced Microwave Scanning Radiometer for EOS (AMSR-E) on Aqua

• Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Terra

• Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud, and land Elevation Satellite (ICESat)

EOS Products for Sea Ice

• Standard and derivable EOS products cover many of the dynamic and thermodynamic processes important for evolution of the sea ice cover at several spatial scales:– Extent, concentration, motion, temperature

(AMSR-E, MODIS)– Snow cover over FY ice, melt onset (AMSR-E)– Albedo, meltponds, leads (MODIS)– Thickness, surface roughness (ICESat)

Beaufort Sea, March 2004Region of Study

BeaufortSea

Alaska

NorthPole

AMSR-E 89V GHz TBs, 1 – 31 March

160

240

TB (

K)

20 cm s-1

AMSR-E 89V TB and Sea Ice

Motion 6.25 km Resolution

2 M

arc

h3

Ma

rch

4 M

arc

h

160

240

2 – 3 March 3 – 4 March

TB (

K)

MODIS Surface Temperature

5 March

270235

Temperature (K)

Clouds

ICESat Sea Ice Thickness

7 MarchTheoretical Thickness (Lebedev) = 16 cm

Lead

Thicker ice on lee side

~18 cm

Integrated Products

• Sea ice dynamics/deformation from motion and thickness

• Thermodynamics – ice growth, turbulent fluxes, salinity flux from concentration, temperature, thickness

• Cross-validation of estimates, e.g., thickness from (1) ICESat, (2) theoretical, (3) surface temperature

Measurement Accuracy

• Ice concentration: 5-10% RMS but higher in marginal ice zone and summer (biases)

• Ice extent: ~10 km from AMSR-E, ~1 km for MODIS

• Ice motion: ~4 km/day RMS from AMSR-E, lower (~1 km/day) from MODIS under clear skies

• Ice thickness: ~50 cm from ICESat (snow cover uncertainties) – R. Kwok, pers. comm.

Derived Quantities Accuracy

• Derived quantities– Turbulent heat fluxes– Salinity flux

• Difficult to asses accuracy requirements – depends on user community– e.g., model sensitivity to parameters– Is 10% RMS okay? 5%?– What about biases? (summer sea ice)

• Difficult to assess accuracy, need validation studies

User Community Requirements

• Small-Scale Processes (e.g., ice deformation, leads)– Spatial/Temporal Resolution (need combination with

models?)• Operational (navigation, native communities, etc.)

– Accuracy – must be able to provide reliable analyses/forecasts

– Timeliness – must be quick enough to be useful– Error assessment - reliability

• Regional/GCM Modeling– Error assessment– Compatibility – accurate parameterization,

spatial/temporal scale, upscaling, gridding, temporal sampling

• Assimilation/Forecasting– All issues crucial– Knowledge of errors

Summary

• New satellite data can be integrated to provide more complete thermodynamic and dynamic picture of the evolution of the sea ice cover

• Integration with other observations– Radarsat and ICESat (Kwok and Zwally, 2004)– Cryosat (snow depth combined with ICESat?)– surface and (sub-surface) observations (buoys,

AWS, ULS, field campaigns, etc.)– Autonomous vehicles (UAV, subs)

• User needs and sensor capabilities need to be considered when creating integrated products