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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