1. The passive microwave sea ice products…. ….oh well…

2. Data Summaries Passive Microwave sensors Near real-time products Polar stereographic grid Near Real-Time DMSP SSM/I Daily Polar Gridded Sea Ice Concentrations (NASA Team) EASE-Grid Near Real-Time SSM/I EASE-Grid Daily Global Ice Concentration and Snow Extent (NASA Team) Archived products National Snow and Ice Data Center (NSIDC) NASA Team and Bootstrap algorithms DMSP SSM/I Daily and Monthly Gridded Sea Ice Concentrations Sea Ice Trends and Climatologies from SMMR and SSM/I AMSR-E/Aqua Daily L3 12.5 km Tb, Sea Ice Conc., & Snow Depth Polar Grids AMSR-E/Aqua Daily L3 25 km Tb, Sea Ice Temperature, & Sea Ice Conc. Polar Grids NASA Team algorithm only Nimbus-7 SMMR Polar Gridded Radiances and Sea Ice Concentrations DMSP-F8 SSM/I Pathfinder Global Level 2 Sea Ice Concentrations Sea Ice Index Other algorithms Northern Hemisphere EASE-Grid Weekly Snow Cover and Sea Ice Extent Version 2 Snow Melt Onset Over Arctic Sea Ice from SMMR and SSM/I Brightness Temperatures Polar Pathfinder Daily 25 km EASE-Grid Sea Ice Motion Vectors NASA Goddard Space Flight Center (GSFC) NASA Team algorithm (modified) Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I Passive Microwave Data Bootstrap algorithm (modified) Bootstrap Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I

3. Daily/monthly SMMR and SSM/I ice concentrations: NASA Team, Bootstrap Daily/monthly SMMR and SSMI ice concentrations for long time series: NASA Team, modified NASA Team, modified Bootstrap Daily AMSR-E: NASA Team 2, Bootstrap (12km), AMSR Bootstrap (25km) Grids: Polar-stereographic: 25 km, 12 km EASE: 25 km Algorithms (1)

4. Algorithms (2) AlgorithmFrequenciesProsConsComments NASA Team 19V,19H, 37V -> PR,GR Independence on temp. (ratios) Sensitivity to layering and thin ice SMMR, SSM/I Mod. NASA Team see NASA Team Algorithm tiepoints adjusted to ensure inter- satellite consistency (relative to SMMR) NASA Team 2 19V,19H, 37V,85V,85H (-> ratios) No layering sensitivity, Wx correction, thin ice correction No data before 1992 SSM/I, AMSR-E Bootstrap19V-37V or 37V-37H No sensitivity to layering Temperature (thin ice) dependence, Tiepoints switch by date SMMR,SSM/I, AMSR-E (12.5km) Mod. Bootstrap see Bootstrap Tiepoints vary to ensure inter-satellite consistency AMSR Bootstrap 6V, 19V, 37VLess temperature dependence Low spatial resolution; no data before 2001 AMSR-E (25 km)

5. Timeseries (climatological) data Arctic and southern ocean sea ice concentration (N.H.: 1901-1995; S.H.: 1973-1990) After 1978 NASA Team Bootstrap ice concentrations from SMMR/SSMI (GSFC) 1978-2002 modified Bootstrap Sea ice concentrations from SMMR/SSMI (GSFC) 1978-2000 modified NASA Team Sea ice index (monthly ice extent, ice concentration) since 1988 NASA Team Sea ice trends and climatologies from SMMR/SSMI since 1978 NASA Team Bootstrap ….and then there are, of course, the “standard” ice concentrations: from SMMR (1978 - 1987) : NASA Team only from SSM/I (1987 – present): NASA Team and Bootstrap from AMSR-E (2004 (at the moment) – present): NASA Team 2, Bootstrap, AMSR Bootstrap

6. NASA Team Bootstrap NASA Team has errors at larger spatial extent while errors in Bootstrap are in areas of larger heat fluxes.

7. Some thoughts (1) Why do users choose a specific algorithm/product? Other publications/applications from peers using specific algorithm? Inter-comparison papers? Transparency/Reproductibility/Understanding of limitations? Lobbying? NSIDC web information? Faster (immediate) access to data via ftp? Is accuracy and temporal consistency necessarily mutually exclusive? Yes, overpass times are different between sensors but… increasing resolution more channels improved algorithms Is occasional “re-analysis” (re-processing) advisable?

8. Need to be careful about indirect effects on algorithm outputs For example: Temperature sensitivity – warmer/colder air temperatures Wetness sensitivity – earlier onset of melt Given the history with these algorithms and the user groups that use either algorithm a decision towards either the Bootstrap or the NASA Team may not be the way to go. (Neither is undoubtedly superior)…and in a way there’s nothing wrong with that as long as users understand limitations. Possible solution: Development of better algorithm Patch – Adjust tiepoints - Reprocess backwards Some thoughts (2)