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

Explore the sea ice mass balance of the Arctic using advanced EOS sensors, data improvements, and model enhancements. Identify gaps in knowledge and ways to fill them for better understanding. Satellite observations, NASA EOS sensors, and integrated EOS products offer valuable insights.

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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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