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A Multi-Sensor, Multi-Parameter Approach to Studying Sea Ice: A Case-Study with EOS Data

A Multi-Sensor, Multi-Parameter Approach to Studying Sea Ice: A Case-Study with EOS Data. Walt Meier. 2 March 2005. IGOS Cryosphere Theme Workshop. SIMBA. S ea I ce M ass B alance of the A rctic NSF organized workshop in Seattle, WA: 28 Feb – 2 Mar, 2005

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A Multi-Sensor, Multi-Parameter Approach to Studying Sea Ice: A Case-Study with EOS Data

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  1. A Multi-Sensor, Multi-Parameter Approach to Studying Sea Ice: A Case-Study with EOS Data Walt Meier 2 March 2005 IGOS Cryosphere Theme Workshop

  2. 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(?)

  3. 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

  4. 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)

  5. 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)

  6. Beaufort Sea, March 2004 Region of Study 240 Alaska Beaufort Sea North Pole TB (K) 160 AMSR-E 89V GHz TBs, 1 – 31 March

  7. AMSR-E 89V TB and Sea Ice Motion6.25 km Resolution 240 2 March 2 – 3 March 3 – 4 March TB (K) 3 March 4 March 20 cm s-1 160

  8. 235 270 Temperature (K) MODIS Surface Temperature Clouds 5 March

  9. Lead ~18 cm Thicker ice on lee side ICESat Sea Ice Thickness 7 March Theoretical Thickness (Lebedev) = 16 cm

  10. 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

  11. 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.

  12. 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

  13. 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

  14. 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

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