1. Development of passive microwave cryospheric climate data records - and a possible alternative for GHRSST W. Meier, F. Fetterer, R. Duerr, J. Stroeve Presented by Florence Fetterer at the 2011 GHRSST workshop, Boulder, CO

2. (GCOS SST/SI) Connection to GHRSST - 2006 slide from S. Anderson GCOS SST&SI working group, Meeting of the sea ice subgroup Søren Andersen, Center for Ocean and Ice, DMI Copenhagen PM CDR best Alternative best Point >> What is optimal for GHRSST- PP is not optimal for a sea ice CDR

3. GHRSST-PP Science Team Meeting, Boulder CO 27-31 March, 2006 3 What does GHRSST-PP want? Our assumptions  High resolution (5-10 km)  Timely (6-12 hours after acquisition)  Fixing the ice edge position accurately is more important than accurate interior pack concentrations  Concentration in the MIZ is important  Uncertainty estimates (these become esp. important if swath data are used)  Want both accuracy, and continuity with existing record (though reprocessing at NODC allows for two data streams)

4. MASIE is a possible alternative for GHRSST N EW ! Collaboration between NSIDC and NIC Based on NOAA IMS product Vis/IR/SAR/PM inputs + human analysis 4 km resolution Daily extent/edge Several formats http://nsidc.org/data/masie/

5. Passive microwave sea ice data 32+ year record  able to track long-term trends Near-complete, daily fields (all-sky conditions) Consistent data source, algorithm, and processing methods  high confidence in comparing sea ice conditions through the years But CDR show-stopper issues remain:  Several algorithm products  Limited quality/error information  Little or no metadata NASA-Team sea ice concentration from the Sea Ice Index, animation at http://nsidc.org/sotc/sea_ice.html

6. Timeline of passive microwave sensors for sea ice 1970 19801990200020102020 ESMR SMMR F8 F11 F13 F17 F18 F19, F20 MIS? AMSR-E AMSR2 AMSR3,4 Nimbus-5 (single channel) Nimbus-7 DMSP NPOESS NASA EOS Aqua JAXA GCOM-W SSM/I SSMIS * May 2009 – Intercalibration of F13 and F17 sea ice products: first time done for near-real-time sea ice data *

7. Current NSIDC Sea Ice Concentration Products NASA Team (NT) 1-byte integer array Flat binary format Concentration scaled 0-250 (quarter %) Land, coast, pole, missing flag values 300-byte header w/ limited metadata No quality/error information NT Antarctic landmask Bootstrap (BT) 2-byte integer array Flat binary format Concentration scaled 0-1000 (tenth %) Land, pole/missing flag values No header, no file level metadata No quality/error information BT Antarctic landmask Sea Ice Concentration CDR Suite of 1-byte integer array for NT, BT and a combined field NetCDF format Concentration 0-100%, (one %. Accuracy about 5-10%) Land, coast, pole, missing, lake flag values Full ISO 19115 standard metadata Extent and data quality fields Consistent landmask in Antarctic Future Sea Ice Concentration CDR

8. Combined sea ice field 1.Use BT to fix the ice edge  Coarse spatial resolution leads to ambiguous ice edge  37 GHz channel has smaller footprint and more sensitive to thin ice o BT edge uses only 37 GHz o NT uses 19 and 37 GHz 2.Within the edge, use Max(BT|NT) for each grid cell  PM concentration generally biased low o NT for thin ice and melt o BT for interior cold temps.

9. Concentration CDR Product Suite Example NT Only BT Only NT > BT BT > NT BT = NT 100 0 % Concentration +100 -100 % BT-NT Conc. Diff. BT Edge NT Edge BT&NT Edge Near-coast # days since melt onset 0200 Combined NT & BT Conc. Sea Ice Extent Quality Field Conc. Diff. Original NASA Team (NT) and Bootstrap products will also be included with same format and scaling 15 March 2007 (SI if conc >15%)

10. 100 0 % Concentration +100 -100 % BT-NT Conc. Diff. # days since melt onset 0200 Combined NT & BT Conc. Sea Ice Extent Quality Field Conc. Diff. 15 July 2007 BT Edge NT Edge BT&NT Edge Near-coast NT Only BT Only NT > BT BT > NT BT = NT (SI if conc >15%)

11. 2007 Sea Ice Extent ArcticAntarctic

12. Next Steps Process F17 SSMIS NT and BT, 2007-present Finish metadata implementation Implement NRT BT Transition CDR to routine NRT processing Consider adding addition algorithms and sensors  AMSR-E  NASA Team 2  Other products

13. How much sea ice is there? NSIDC U. Bremen JAXA DMI ArcticROOS U. Illinois NIC (MASIE)

14. Satellite-derived Sea Ice Products Community Workshop 15-16 March 2011, NASA Goddard, Greenbelt, MD Bring together algorithm/product developers to discuss ways to work together and standardize products where possible  e.g., an ensemble sea ice extent estimate? Also bring in users (modeling, operational communities, etc.)  What do they need? – what SI product used now? Its limitations?  How can passive microwave sea ice products better meet user needs? – data quality information, error estimates, file format, grid/projection, metadata Supported by WCRP Climate and Cryosphere (CliC) Please see me at the breaks and I’ll collect input, or contact Walt: walt@nsidc.org

15. GHRSST-PP Science Team Meeting, Boulder CO 27-31 March, 2006 15 Satellite Passive Microwave for Sea Ice Bootstrap, Cal-Val, NASA Team 2, and NASA Team algorithm ice edge contours compared with SAR imagery. The Cal-Val algorithm is probably best for concentrations at the ice edge. But it does poorly in other areas. Figure from: Point >> What is optimal for GHRSST-PP is not optimal for a sea ice CDR 5% 15% 50% 90%

16. Satellite-derived Sea Ice Products Community Workshop 15-16 March 2011, NASA Goddard, Greenbelt, MD Current and upcoming products/projects to be discussed:  EUMETSAT Ocean and Sea Ice Satellite Application Facility  ESA Climate Change Initiative Sea Ice Essential Climate Variable  NOAA Sea Ice Climate Data Record  JAXA GCOM-W AMSR2 sensor – launch in late 2011 Contact Walt if interested, walt@nsidc.org

17. Satellite-derived Sea Ice Products Community Workshop 15-16 March 2011, NASA Goddard, Greenbelt, MD Issues to be discussed:  Product conventions o ice edge threshold (15%?, 30%?) o Climatology period (1979-2000,? 1979-2008?) o Grid/projection (polar stereographic, EASE)  Format – NetCDF, HDF, binary, etc.  Collaboration o Joint white paper or journal review paper o Ensemble contribution to IPCC AR5, etc  Metadata  Integration with other climate products (e.g., SST)