REANALYSIS OF SEA ICE CONCENTRATION FROM THE SMMR AND SSM/I RECORDS Søren Andersen, Lars-Anders Breivik, Mary J. Brodzik, Craig Donlon, Steinar Eastwood, Florence Fetterer, Jacob Høyer, Walter N. Meier, Leif Toudal Pedersen, Nick Rayner, John Stark, Julienne Stroeve, Rasmus Tonboe

Outline History and background Scientific advances –Algorithm selection –Atmospheric correction –Error estimates Processing and data set outline Plans and schedule

History Present sea ice concentration time series products are extremely simple and contain little to no meta data and uncertainty information Products are based on level 3 radiances precluding detailed scrutiny and satellite intercomparison A number of products exist but there is currently no consensus on relative merits or best practices. Following OSISAF activitites to reprocess the SSM/I time series, a workshop was held at NSIDC in March 2007 to: –Exchange views and results –Review and adapt OSISAF plans –Define a shared state of the art, traceable data set including the SMMR time series ( )

Background Error sources –Atmospheric –Ice/snow emissivity –Mixing of footprints –Sensor noise (<2%) Thin ice Comiso et al., 1997

Variation of Geophysical Errors Atmospheric errors are important at low ice concentrations Ice emissivity errors are important at high ice concentrations

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 ff.) Atm. stdev Bristol Bootstrap + Mainly: Meier (2005); Toudal (2006); Andersen et al. (2006+7)

Atmospheric correction 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

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

Error estimates Spatially and temporally varying error estimates: Error due to atmospheric contribution, estimated from ERA 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 Empirical Atmosphere Ice tiepoint Combined C Uncertainty Water Ice

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.

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.

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