A Multi-Sensor, Multi-Parameter Approach to Studying Sea Ice: A Case-Study with EOS Data Walt Meier 2 March 2005IGOS 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 (?)

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 2004 Region of Study Beaufort Sea Alaska North Pole AMSR-E 89V GHz T B s, 1 – 31 March T B (K)

20 cm s -1 AMSR-E 89V T B and Sea Ice Motion 6.25 km Resolution 2 March 3 March 4 March – 3 March3 – 4 March T B (K)

MODIS Surface Temperature 5 March Temperature (K) Clouds

ICESat Sea Ice Thickness 7 March Theoretical 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