GCOS SST&SI Working Group

1. GCOS SST&SI Working Group Chairs Nick Rayner Tom Smith Executive committee Ken Casey Elisabeth Kent Alexey Kaplan Craig Donlon Ed Harrison Dick Reynolds Sea ice subcommittee Søren Andersen, DMI/EUMETSAT OSISAF Florence Fetterer & Walt Meier, NSIDC Tony Worby & Steve Ackley, ASPeCt Per Gloersen, NASA GSFC Cathleen Geiger, CRREL/U. Delaware John Stark, UK Met Office Vasily Smolyanitsky, AARI/JCOMM ETSI Pablo Clemente-Colon, NIC Mark Drinkwater, ESA Stefan Kern & Dirk Notz, U. Hamburg Jinro Ukita, Chiba University

2. Outline • Background and summary of objectives • Organisation • Motivation and mission • Illustrative examples • Proposed list of activities • Status • Events • Reports • Intercomparison • Outlook • Recent relevant results

3. Organisation

4. Motivation from a climate data user perspective Uneven data coverage as well as inconsistencies between different passive microwave sea ice timeseries and ice charts have been identified by climate community constructing longterm SST&SI analyses: No uncertainty estimates in existing ice analyses. An overwhelming number of data sets/algorithms with poorly known relative and absolute skills. For microwave data: Principal homogeneity issues with short overlap periods and different equator crossing times. The popular NSIDC gridded dataset has only 16 days of overlap between DMSP F8 and F11 SMMR and SSM/I overlap is 22 days For ice charts: Changes in analysis capabilities and practices are not always well documented Major challenge to reconcile ice chart, observation and satellite records. The Antarctic has significantly less historical coverage than the Arctic Motivation and mission Mission To provide analysis and recommendations on long term consistent sea ice fields with uncertainty estimates for use in SST & SI analyses. Initially focus on ice concentration but consider ice thickness as methods and data sets mature.

5. Rapid changes in MY sea ice Nghiem et al., 2006

6. Arctic trends Analysis by Kaleschke, presented in Andersen et al. 2006 (b). Merged ESMR – SSM/I [Cavalieri et al.,2003] with NSIDC sea ice index. Recent developments have no correlation with AO, but Maslanik et al. (2007) suggest atm. circulation may still be significant Meier et al. – Ann. Glaciol. (46) 2007: Confirms the uniqueness of recent decline in ice coverage, while illustrating the sensitivity of trends to period and length of time series.

7. Trends in Antarctic NSIDC sea ice indicator May be due to increased snow ice formation with increased Precipitation. (Ice mass growth is a balance between insulation and snow ice), Powell et al., 2005.

8. NIC’s Sea Ice Climatology 1976 1993 Example from Partington et al showing change in chart detail , 1976 to 1993 Over time: Increase in quality, quantity, and effective resolution of charts Courtesy Florence Fetterer, NSIDC

9. Differences in trends Ice chart and Microwave trends Arctic area trends from 7 Microwave algorithms 1987-2004 -42611 -58111 -33230 Nearly a factor 2 between lowest and highest estimate 1991-2004 -42611 Courtesy Nick Rayner, Hadley Centre

10. Arctic Antarctic Some blind spots, the Antarctic • Available time series • Arctic has systematic observations back to late 1800’s • Antarctic data is extremely scattered and spotty up to satellite era • GDSIDB Arctic: 1901-1997 • GDSIDB Antarctic: 1973-1991 HadISST extents (Rayner et al, 2003) Gaps German Atlas Russian Atlas

11. Focused activities • Systematic and comprehensive intercomparison of sea ice analyses • Help characterize important temporal and spatially distributed differences • Provide starting points for further investigation and cooperation • Help evaluate effects of different assumptions • Provide a step towards meaningful accuracy and uncertainty estimates • Error estimates • Develop and promote methods for standardized error estimates for both ice charts and satellite analyses • Data • Provide overview of the numerous data sets available • Promote easy and standardized access to field and ship observations • Examine data gaps and, if possible, recommend mitigation actions

12. Implementation The full level of activity will depend on level of community commitment and funding. However, some initial activities are already committed to: • Demonstration of intercomparison of a limited set of IC products • Products repository in common simple netCDF format • Develop statistics and comparison procedures (initial short list exists) • Presentation of analyses and download of data • Ice chart based ice edge uncertainty project (NSF, Geiger) • Outcome of IICWG 2006 • First step towards a more informed use of ice chart information in climate science • Not to forget, progress through cooperation • Structuring and collection of in situ observations and use of ice chart data for ice thickness in ASPeCt • Structuring, collection and documentation through IICWG, ETSI • Scientific coordination through CliC/WCRP and others.

13. Overview of events Documents Intercomparison Status

14. Recent events • Group meetings: • Reformed in Exeter October 2005 with a wide initial representation in sst and the decision to form a specific subgroup on sea ice • Inaugural sea ice meeting in Boulder, March 2006, established initial plans • Participation in IICWG in Helsinki, September 2006, featuring a dedicated morning session in plenary and several break outs. Resulting in an IICWG supported proposal to NSF for error analysis of ice chart based ice edge estimates. • Interactions with other groups and bodies • Presentation at Antarctic Sea Ice thickness Workshop, Hobart (July 2006) • IGOS-Cryo meeting, Noordwijk (October 2006) • JCOMM-ETSI meeting, Geneva (March 2007), plans for a common ice analyst workshop in 2008. • The SI group is leaving the formative phase, entering ops.

15. Reports

16. Intercomparison • Intercomparison is not a new concept in sea ice retrieval. • Bad news: • It can be implemented in many ways. • Small details and assumptions (e.g. spatial and temporal coverage, products considered, filtering/averaging) may affect results. • Interpretation is often difficult (e.g. in terms of which product is more correct) • Good news: • Comparison of products efficiently reveals signatures of underlying algorithm and system sensitivities. Links well with efforts to understand the underlying processes (e.g. radiative/microphysical, retrieval/classification) • Experience from existing intercomparison exercises may help identify best practices and limitations.

17. Systematic intercomparison • netcdf gdsidb_blended-ps { • dimensions: • ni = 304 ; • nj = 448 ; • time = 588 ; • variables: • float lon(nj, ni) ; • float lat(nj, ni) ; • short time(time) ; • time:units = "Months since Reference_time" ; • short ct(time, nj, ni) ; • ct:long_name = "Ice concentration" ; • ct:units = "%" ; • ct:_Fillvalue = 165s ; • float pix_area(nj, ni) ; • pix_area:long_name = "Pixel area" ; • pix_area:units = "km^2" ; • // global attributes: • :Reference_time = 1950. ; • :Data_set_name = "GDSIDB blended analysis 1950-1998" ; • :Version = "1.00" ; • In practice the idea is: • Get data in common simple, self-contained format, example: • Compute statistics and intercomparison products • Make comparison products anddata sets easily available • The hard part: Interpret the results

18. Example problem: Regridding • Nearest neighbour regridding of GDSIDB product from 0.25 geographical to 25 km polar stereo projection • 2.5 % difference in extent trend (-13.9 to -14.2 x 103 km2/year) • 6.5 % difference in area trend (-8.9 to -9.5 x 103 km2/year) Original Regridded Extent Area

19. Development snapshot • Intercomparison implemented in Python using matplotlib, numpy and Nio packages. • Possible evolution to web application via CherryPy and TurboGears • Data sets: HadISST, NASA/Team, Bootstrap, GDSIDB Initial list of products • Linear trends of monthly mean values of sea ice extent and area results in a measure of the spread in estimated retreat or increase in the sea ice cover. Taking one product as reference can be useful. • Maps of linear trend in concentration or sea ice persistence provide the spatial structure of differences in estimated sea ice trends. • Per pixel range of concentration based on several products or maps of anomaly with respect to wintertime average ice concentration provide spatial structure of single algorithm results. • Maps of differences between algorithms on various time scales provide the spatial structure of inter-algorithm differences. ÷ Intended to augment SST intercomparison system at NODC

20. Outlook • IPY • Snapshot activities will make large quantities of high resolution EO data available and facilitate comprehensive validation activities • Field activities will help in developing and validating models that relate snow and ice parameters to emissivity • Massive deployment of buoys will at least help relating observed signals to meteorological conditions and ice drift patterns • The systematic data management will make the application of the data possible. • New ice thickness measurement capabilities • Space based altimeters are becoming operational (IceSAT and follow-on, Cryosat-II, Sentinel-3) • Development of AUV systems is picking up speed, in part thanks to IPY • The ASPeCt data set is close to 25000 observations spanning more than 2 decades in the Antarctic and still growing • General movement towards sustained operational satellite programmes, e.g. ESA Sentinels, (JAXA GCOM?); also a movement towards free access to SAR data. • SMMR+SSM/I is approaching 30 years of continuous operation - but there will most likely be a gap in AMSR class passive microwave observations and Seawinds scatterometer continuity is very unlikely.

21. Conclusions • The group is coming out of its formative phase • Group members span ice charting, in-situ, satellite observations and to some extent modelling disciplines • Activities and organisation have been outlined and agreed in a white-paper document • Many of the defined activities have basic support from existing activities of members. • An expanded set of activities, including standardisation of error estimates, interpretation of differences and thorough investigations of ice chart records, may require external funding, some initiatives have started already. • The group is seen to benefit from and add to the momentum of existing groups like ASPeCt and IICWG • In the longer term, it is hoped that the group, in cooperation with the ice charting community, may help lessen the gap between ice chart and satellite observations to achieve longer data sets of sea ice concentration with improved confidence.

22. Examples of recent results by group members

23. ASPeCt: Tracks of 83 “good” voyages Worby et al., 2007

24. Annual mean ice thickness including ridges and open water 2.5 x 2.5º grid Worby et al., 2007

25. AMSR-E v ASPeCt snow thickness data Worby et al., 2007

26. NT-CHART BT-CHART TOTAL MULTIYEAR FIRST-YEAR THIN 4 OCT 1999 28 FEB 2000 % % +50 5 15 25 35 45 55 65 75 80 85 90 95 100 -50 0 PM and chart total ice concentration difference, for two PM algorithms: Total concentration from ice charts and partial concentrations by ice type – multiyear, first-year, and thin ice. Charts provide more information and are more accurate, but are less consistent than PM. NT = NASA Team algorithm BT = Bootstrap algorithm Arctic Sea Ice from Passive Microwave (PM) Sensors and Operational Ice Charts From Meier, Fetterer, Fowler, Fall AGU 2006

27. MEMLSI simulations of ice concentration NASA Team: sensitive to layer contrast. Comiso frequency: moderately sensitive to scattering. Near 90 GHz: moderately sensitive to deep scattering, sensitive to layer contrast. Upper snow-layer density 100-410kg/m3 Above ice correlation length 0.14-0.32mm NASA Team Comiso frequency Near 90GHz Scattering Layering Tonboe et al. 2006

28. Winter concentration anomalies BRI 31 Oct 2000 – 31 Mar 2001 CP NT2 Polarisation N90 NT CF Spectral Gradient Ice/snow Layering Less Ice/snow Layering Andersen et al. 2007

29. Backup material

30. Trends

31. Satellite trend • Analysis by Kaleschke, presented in Andersen et al. 2006 (b). • Merged ESMR – SSM/I [Cavalieri et al.,2003] with NSIDC sea ice index

32. Rapid changes in MY sea ice Nghiem et al., 2006

33. 2 2 km km *1000 *1000 1600 1200 2000 y = -1.2603x + 989.27 1400 1800 y = - 5.1265 x + 1270.4 1000 1200 1600 1000 1400 800 800 1200 y = -2.5993x + 600.03 600 1000 600 y = - 1.2033 x + 1092 400 800 200 600 400 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 y = -0.9572x + 299.6 400 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 200 y = -2.5367x + 183.06 0 1968 1973 1978 1983 1988 1993 1998 2003 Temporal analysis of ice index data: variability and linear trends in August for Eurasian (1900-2003) and Canadian Arctic (1968-2004) Greenland, Barents, Kara Laptevs, Eastern-Siberian, Chukcha Western CA (blue) Eastern CA (green) East Coast (red) (April) Hudson Bay (light blue) From: V. Karklin, Z.Gudkovich, I.Frolov, V.Smolyanitsky, J.Falkingham “Interannual variability of Eurasian and Canadian Arctic sea ice in the 20th century and expectations for the 21st century”. JCOMM-II Scientific Conference “Operational Oceanography and Marine Meteorology for the 21st Century”. Septembr 15-17th, Halifax, Nova Scotia, Canada.

34. Wavelet analysis of sea ice extent variations for Eurasian Arctic Seas (based on 1900-2003 period) and Canadian Arctic Seas (based on 1968-2004 period) in August (red – more ice, blue – less ice) Greenland Barents Kara Laptev Eastern-Siberian Chukcha Western CA Eastern CA Hudson Bay

35. Trends in Antarctic NSIDC sea ice indicator May be due to increased snow ice formation with increased Precipitation. (Ice mass growth is a balance between insulation and snow ice), Powell et al., 2005.

36. Differences

37. NIC’s Sea Ice Climatology Courtesy Florence Fetterer, NSIDC in 1996/1997 , NIC - Transitioned to digital imagery (OLS/AVHRR) and digital analysis in GIS format Started using SAR data in tactically significant areas Now, NIC uses Quicksat to compensate for deficiencies in SSM/I

38. Chart vs. NASA/TEAM SSM/I Agnew & Howell, 2003

39. Differences in trends Arctic ice area trends from 7 Microwave algorithms 1987-2004 1991-2004 Andersen et al., 2007

40. Error sources

41. Atmospheric Water vapour Wind Cloud liquid water Andersen et al., 2006

42. Atmospherically induced stdev Bristol Bootstrap

43. Same with clouds Bristol Bootstrap

44. Empirical fit • Collocation of large number of SSM/I passes gives following relation (Schyberg): σ=0.04+0.07(C(1-C)/0.25) Schyberg Combined Atmosphere Ice tiepoint

45. MEMLSI simulations of snow effects MEMLSI emissivity model setup to explore effects of layering and coarse grains in the snow layers above sea ice. 1 2 Layer 1 snow density 100-410kg/m3, varying layer contrast btw. layers 1 and 2 Layer 3 correlation length (grain size) 0.14-0.32mm, simulating effect of depth hoar NASA Team Comiso frequency Near 90GHz Scattering http://saf.met.no Layering

46. Footprint convolution errors Deconvoluted Reference True low res. Combined convolution and simple averaging Limaye et al. (2006): Particularly important in gradient areas, i.e. ice edge and coast. Additional errors come from mixing of Channels with different footprint size

47. In summer: Melt ponds and others In summer, melting snow and ice forms open freshwater ponds on top of the ice that, in the microwave regime, can be distinguished from the open ocean only by salinity. Additional complications occur e.g. due to refreezing of the ponds. Sea ice topography exerts an important control on the spatial extent and depth of the ponds. Spatial coverage can range from 20-50% Russ Hopcroft, from the University of Alaska Fairbanks, takes a sip from one of the many meltponds scattered around the ice. (Photo courtesy of Ian MacDonald.) – From NOAA Ocean Explorer site

48. Applications/impacts

49. Impact in climate models Singarayer et al. (2005) Fig.2+ Fig.3: Sea ice uncertainty is large but ocean - atm. gradient is small during summer. Other contributions: Rinke etal. (2006): Influence of sea ice on atmosphere. Parkinson et al.: Impact on climate models (2001)

50. Assimilation over sea ice Estimation of the surface emissivity