Sea ice concentration alphabet soup: CDRs, ECVs, ESDRs

Transcription

1 Sea ice concentration alphabet soup: CDRs, ECVs, ESDRs Walt Meier, NASA Goddard Polar Space Task Group Fourth Session 30 September 2014

2 Formal Definition Climate Data Record (CDR) noun, A time series of measurements of sufficient length, consistency, and continuity to determine climate variability and change (U.S. Nat l Research Council Report on Climate Data Records from Environmental Satellites, 2004) Synonyms: Earth Science Data Record (ESDR) [NASA], Essential Climate Variable (ECV) [ESA]

3 Practical Definition At least 30 years Intersensor calibration Uncertainty estimates at grid-cell level Detailed documentation Metadata Self-describing file formats Fully reproducible

4 PM sea ice timeline ESMR Nimbus-5 (single channel) SMMR Nimbus-7 F8 F11 SSM/I F13 DMSP F17 SSMIS F18 F19 F20 NASA EOS Aqua AMSR-E JAXA GCOM-W AMSR2 ESA AMSR3,4 MetOp

5 NASA Goddard Products NASA Team (NT) and Bootstrap (BT) 1978-present for SMMR-SSMI-SSMIS Archived at NSIDC NSIDC provides NRT version of NT; Goddard produces NRT BT plots and images benchmark products widely used in the science and other communities Long-term, high quality control, including manual corrections Spatial and temporal interpolation (not explicitly flagged) Do not meet all CDR criteria AMSR-E and AMSR2 (in development) products NASA Team 2 (NT2) and Bootstrap

6 NOAA/NSIDC CDR Uses NT/BT algorithm products as the basis Suite of concentration and ancillary products (including a simple combined CDR parameter) Grid-cell level uncertainty estimates based on differences in algorithms and spatial variability Grid-cell level data quality flags (melt, etc.) No spatial/temporal interpolation (may be added later, with associated flags) self-describing file format, NetCDF4-CF Automated processing, fully reproducible, software available Archived at NSIDC, access from NOAA CDR website (non-cdr-compliant estimates provided for period)

7 CDR fields, Arctic 2007

8 CDR comparison with AVHRR 2 July 2007 CDR St. Dev. AVHRR Visible 3-Day Composite NASA Team Bootstrap 0 Concentration (%) Spatial St. Dev. (%)

9 ESA Climate Change Initiative (CCI) ECV Similar to EUMETSAT OSI-SAF product Hybrid Bristol-Bootstrap algorithm Atmospheric correction using RTM and ECMWF ERA- Interim reanalysis NetCDF4 Uncertainty fields Algorithm uncertainty (spread of ice/water tiepoints, atmosphere from ECMWF reanalysis) Gridding uncertainty (sensor footprints grid) Separate SSMI and AMSRE products Phase 1 (now available): Phase 2 (in development): 1978-present

10 ESA CCI sea ice 15 Nov1995 ESA CCI Sea Ice Product User Guide, Lavergne and Rinne,

11 NASA Team 2 approach 5 T B channels 6 ratios 12 pre-set atmospheres and an atmospheric RT model 3D look-up tables with modeled ratios two ice types, and the 12 atmospheres - FY/MY ice type - Ice type C: new ice or deep snow (GR3719 threshold) Observed and modeled ratios, compared using R: NT2 sea ice concentration corresponds to where R is minimum

12 Approach for Uncertainty Developed by Ludovic Brucker (NASA Goddard/USRA GESTAR) Brucker et al., IEEE Trans. Geoscience and Remote Sensing, 2014 δr is a measure of agreement between observed T B s and modeled T B s with atmospheric contribution Variability of concentration as δr min is approached is an indication of concentration uncertainty Standard deviation of concentration over last N iterations before δr min is reached Uncertainty estimate derived entirely within the algorithm framework no ancillary data Derived uncertainty is relative, not absolute

13 Example of NT2 SIC iteration IC (%) δr Central Arctic Baffin Bay Iteration: 100 0

14 Concentration and relative uncertainty Ice concentration Relative uncertainty % % Brucker et al., IEEE TGRS, 2014 Courtesy L. Brucker

15 AMSR2 sea ice products JAXA standard product Bootstrap algorithm Operational, available from JAXA NASA standard products Bootstrap and NT2 algorithms in development provide consistency with AMSR-E standard products + possible enhancements NOAA operational product combined Bootstrap/NT2 EASE2 grid

16 NT 2 BT Max Concentration BT-NT2 Concentration Arctic AMSR2 concentration and 15 Mar 2013 differences Mar Sep Sep

17 NT2 BT Max Concentration BT-NT2 Concentration Antarctic AMSR2 concentration and 15 Mar 2013 differences Mar Sep Sep

18 Intercalibration issues for PM Previous overlap periods were short (<1 month) Sea ice properties may vary considerably over year as does calibration In some cases, longer overlaps could be used Spatial resolution limits intercalibration AMSR vs. SSMI Higher trend uncertainty than generally assumed New versions of source TBs available for SMMR, SSMI, and SSMIS Antarctic F11-F8 sea ice extent Longer difference intercalib periods Intercalibration from early-dec data only Meier et al, IEEE TGRS, 2011

19 Obs4MIPs Observations for Model Intercomparison Projects Initiated by NASA JPL, but endorsed by WCRP Data Advisory Council Meeting in DC, April 2014 Provide satellite data in a framework usable by CMIPs Data formats and grids (NetCDF3) Uncertainty estimates Documentation Working with NOAA, NSIDC, and JPL to provide sea ice concentration CDR

20 Multisensor products Using different sensors can improve improve performance Different capabilities in different conditions Spatial resolution of imagery is perhaps most signficant Improved observations improve model performance Canadian Archipelago, June 2008 NRL ArcticCap operational forecast model sea ice edge error SSMI AMSR-E, regridded, ver. 1 AMSR-E, regridded, ver. 2

21 Multisensor products AMSR-E MODIS 27 June 2008 Courtesy Bill Johnson and Li Li, NRL Combined

22 Conclusion Algorithms mature, general performance well-understood For best quality, manual QC required, but this violates CDR paradigm Uncertainty estimates still maturing, require further validation/calibration Integrating 25 km PM timeseries with AMSR record, multi-stream approach? long-term CDR at 25 km from SMMR-SSMI-SSMIS maximum consistency Shorter high-res CDR at 12.5 km or 10 km from AMSR best PM product at any given time Multisensor products: PM-vis/IR best you can do at any given time

23 Conclusion Algorithms mature, general performance well-understood For best quality, manual QC required, but this violates CDR paradigm Uncertainty estimates still maturing, require further validation/calibration Integrating 25 km PM timeseries with AMSR record, multi-stream approach? long-term CDR at 25 km from SMMR-SSMI-SSMIS maximum consistency Shorter high-res CDR at 12.5 km or 10 km from AMSR best PM product at any given time Multisensor products: PM-vis/IR best you can do at any given time Thank you! Photo by Terry Haran, NSIDC