1. The joint NSIDC and EUMETSAT sea ice re-analysis Søren Andersen, Lars-Anders Breivik, Mary J. Brodzik, Craig Donlon, Gorm Dybkjær, Steinar Eastwood, Florence Fetterer, Jacob Høyer, Walter N. Meier, Leif Toudal Pedersen, Nick Rayner, John Stark, Julienne Stroeve, Rasmus Tonboe

2. Sea ice: a component in our climate system The current sea ice extent measured with passive microwave radiometers (NSIDC) The last 35 years trend in sea ice extent measured with passive microwave radiometers (Lars Kaleschke)

3. Sea ice concentration estimation errors Error sources –Atmospheric –Ice/snow emissivity –Mixing of footprints –Sensor noise (<2%) Comiso et al., 1997 The errors due to atmospheric emissivity and surface roughness are largest at low ice concentrations The errors due to snow/ice surface emissivity variability are largest at high ice concentrations

4. Algorithm selection Algorithm selection is mainly based on: –Comparison to 59 classified SAR scenes, comparison of high concentration variability and outcome of tiepoint study –Sensitivity study based on radiative transfer modelling over Open Water –Comparison to AVHRR over the Arctic marginal seas –Taking into account 8 algorithms Combination of –Bristol (high conc., 1978 ff.) –TUD (85 GHz high res, 1991 ff.) –Bootstrap (low conc. 1978 ff.) Atm. stdev Bristol Bootstrap + Mainly: Meier (2005); Toudal (2006); Andersen et al. (2006+7)

5. Different algorithms – different sensitivity 1.Find an algorithm with low sensitivity to errors 2.Find an algorithm with low sensitivity to errors that we can not correct for Problems: A. Climate trend: The Arctic sea ice extent and area are changing together with the Arctic atmosphere and sea ice emissivity, this is affecting the trend. B. Correlation to high resolution reference: SAR/ vis-IR/ ship obs/ RGPS: There is no correlation between the high concentration radiometer ice concentration and the reference variability, some algorithms are better than others but none are adequate (at near 100%). Error-bars are needed

6. Winter concentration anomalies 31 Oct 2000 – 31 Mar 2001 Layering Scattering NT CPNT2 N90 BRI CF Pol. Gradient

7. Different trends Observed trends in a) ice area and b) ice extent during winter (Oct. – Apr.) for the SSM/I dataset excluding the F8 satellite (1991-2004, black) and the entire dataset (1987-2004, grey). The 85GHz channels were not reliable on F8. Bars show ±1 STDEV. 22% Difference due to emissivity 14% difference due to atmosphere

8. Atmospheric correction is feasible Based on estimates of wind, water vapour, surface temperature and potentially cloud liquid water. Currently based on ECMWF ERA-40 data. Calculates corrections to all brightness temperatures and ice concentrations Andersen et al. RSE 2006

9. The variable ice emissivity Output from a thermodynamic model developed for microwave simulation applications is used as forcing to a sea ice emissivity model The sea ice emissivity during winter is quite variable.

10. Model output comparison Simulated multiyear ice effective emissivity is compared to effective emissivity derived from SSM/I and NWP surface temperature data. The simulated mean is greater than the measured mean. The dash dotted line is the mean for January to April. The simulated gradient and polarisation ratio compared to the ‘NASA Team triangle’ spanned by standard tie-points for first-year ice, multiyear ice and open water. Both polarisation and gradient are realistic in the 18 to 36GHz range. Profiles nearest Nares and Fram straits Near 100% IC

11. Tie points Tie points are derived with error bars: –Prerequisite to estimating uncertainty in ice concentrations Tie points are determined dynamically: –Offers a consistent way to reconcile intersensor differences –Takes into account interannual and seasonal signature variations Melt onset Interannual variation Uncertainty Water Ice

12. Error estimates Spatially and temporally varying error estimates: Error due to atmospheric contribution, estimated from ERA- 40 1987-2003 data Error due to sea ice emissivity uncertainty Error due to footprint mixing and resolution artefacts in marginal ice zone, estimated empirically from local gradient B Atm. stdev Bristol Bootstrap A smearing Atmosphere Ice tiepoint Combined C Uncertainty Water Ice

13. Processing Processing is aimed at maximum transparency: –All output is based on netcdf following the cf convention where applicable –Level2 chain contains no irreversible processing steps. All changes and additions are appended as new variables to netcdf orbit ”super” files –Level3 processing is highly customisable.

14. Output products Level 2 one file per orbit: Ice concentrations (3 base algorithms), Brightness temperatures, atm. correction, melt flags, weather filter, sea ice (type) probabilities, error estimates platform metadata. –Inclusion of ice and ice type probabilities to allow for later extension in a Bayesian scheme with e.g. scatterometer records. –Brightness temperatures may be stripped prior to general distribution due to copyright restrictions. Level 3, two daily products: 1.Common climate oriented data set, common distribution One product per satellite in EASE grid: Ice concentration, atm. correction, sea ice probability, combined error estimate, weather filter flag, melt flag, surface temp., wind, water vapour 2.OSISAF operational type For use in model testing, hindcasts, etc.

15. Schedule 0 20% 40% 60% 80% 100%

16. Level 3 A level 3 dataset including error-bars will be available on the NSIDC website A similar dataset will be available on the EUMETSAT SAF website. This dataset will be updated daily using the same processing chain as the one used in the re-analysis. ECMWF and the Hadley centre have expressed interest We encourage everyone interested to contact us: rtt@dmi.dk