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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/tc-2020-35
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-2020-35
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 20 Feb 2020

Submitted as: research article | 20 Feb 2020

Review status
A revised version of this preprint was accepted for the journal TC and is expected to appear here in due course.

Satellite Passive Microwave Sea-Ice Concentration Data Set Intercomparison for Arctic Summer Conditions

Stefan Kern1, Thomas Lavergne2, Dirk Notz3, Leif Toudal Pedersen4, and Rasmus Tage Tonboe5 Stefan Kern et al.
  • 1Integrated Climate Data Center (ICDC), Center for Earth System Research and Sustainability (CEN), University of Hamburg, Hamburg, Germany
  • 2Research and Development Department, Norwegian Meteorological Institute, Oslo, Norway
  • 3Institute for Marine Research, University of Hamburg and Max-Planck Institute for Meteorology, Hamburg, Germany
  • 4Danish Technical University, Lyngby, Denmark
  • 5Danish Meteorological Institute, Copenhagen, Denmark

Abstract. We report on results of a systematic inter-comparison of 10 global sea-ice concentration (SIC) data products at 12.5 to 50.0 km grid resolution from satellite passive microwave (PMW) observations for the Arctic during summer. The products are compared against SIC and net ice-surface fraction (ISF) – SIC minus the per-grid cell melt-pond fraction (MPF) on sea ice – as derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite observations and observed from ice-going vessels. Like in Kern et al. (2019), we group the 10 products based on the concept of the SIC retrieval used. Group I consists of products of the EUMETSAT OSI SAF and ESA CCI algorithms. Group II consists of products derived with the Comiso bootstrap algorithm and the NOAA NSIDC SIC climate data record (CDR). Group III consists of ARTIST Sea Ice (ASI) and NASA Team (NT) algorithm products and group IV consists of products of the enhanced NASA Team algorithm (NT2). We find wide-spread positive and negative differences between PMW and MODIS SIC with magnitudes frequently reaching up to 20–25 % for groups I and III and up to 30–35 % for groups II and IV. On a pan-Arctic scale these differences may cancel out: Arctic average SIC from Group I products agrees with MODIS within 2–5 % accuracy during the entire melt period from May through September. Group II and IV products over-estimate MODIS Arctic average SIC by 5–10 %. Out of group III, ASI is similar to group I products while NT SIC under-estimates MODIS Arctic average SIC by 5–10 %. These differences, when translated into the impact computing Arctic sea-ice area (SIA), match well with the differences in SIA between the four groups reported for the summer months by Kern et al. (2019). MODIS ISF is systematically over-estimated by all products; NT provides the smallest (up to 25 %) over-estimations, group II and IV products the largest (up to 45 %) over-estimations. The spatial distribution of the observed over-estimation of MODIS ISF agrees reasonably well with the spatial distribution of the MODIS MPF and we find a robust linear relationship between PMW SIC and MODIS ISF for group I and III products during peak melt, i.e. July and August. We discuss different cases taking into account the expected influence of ice-surface properties other than melt ponds, i.e. wet snow and coarse grained snow / refrozen surface, on PMW observations used in the SIC retrieval algorithms. Based on this discussion we identify the mismatch between the actually observed surface properties and those represented by the ice tie points as the most likely reason for i) the observed differences between PMW SIC and MODIS ISF and for ii) the often surprisingly small difference between PMW and MODIS SIC in areas of high melt-pond fraction. We conclude that all 10 SIC products are highly inaccurate during summer melt. We hypothesize that the un-known amount of melt-pond signatures likely included in the ice tie points plays an important role – particularly for groups I and II – and suggest to conduct further research in this field.

Stefan Kern et al.

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Stefan Kern et al.

Stefan Kern et al.

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Latest update: 13 Jul 2020
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Short summary
In winter, satellite passive microwave imagery is excellent for weather- / daylight-independent monitoring of Arctic sea ice but in summer it is challenged by melting snow and melt ponds on sea ice. We compare products of 10 algorithms used for such monitoring with independent satellite and ship-based ice-cover data. All products disagree with these data with large regional and inter-product differences. We hypothesize inadequate treatment of melt conditions in the algorithms as the main reason.
In winter, satellite passive microwave imagery is excellent for weather- / daylight-independent...
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