between the atmosphere, ocean and sea ice, as well as on the ecosystem. Thanks to the collaboration between international experts, the one-year-long ice drift past the North Pole will take climate research

dynamo.html _blank>Click here for further information January 2019 2019 is a big year for the North Sea Benthos time series. The time series, which was initiated by Eike Rachor in 1969 turns 50 this year

ice-covered Fram Strait between Greenland and Svalbard, which they call their HAUSGARTEN. The deep-sea observatory is the first, and still the only one of its kind for year-round physical, chemical and

Sascha Willmes, Fabian Reiser, Günther Heinemann Environmental Meteorology High-resolution daily sea-ice concentration from MODIS thermal infrared imagery, 2002-2019: Current status and available products [...] from MODIS TIR data Environmental Meteorology SIC from Potential Open Water Ice Surface Temperature Sea Ice Concentration Potential artefacts BUT: Masking temperature anomaly artefacts is crucially important [...] straight-forward, BUT: IST artefacts (mostly due to unrecognized clouds) cause low SIC areas, where sea ice was actually present. An additional masking is necessary 3 Environmental Meteorology Approach:

thickness redistribution modeling h2 h1 Thermodynamic growth Dynamic growth 3 Haas, Sea Ice Book, 2017 Results: Sea ice thickness distribution Thickness distribution of the Polynya after one month 0.9 [...] 20190617_chaas_albedyll_16_9 Thickness and Deformation in the 2018 North Greenland Polynya Haas C., L. v. Albedyll, S. Hendricks, T. Krumpen, F. Kauker, J. Rohde Feb 13 – Mar 31, 2018 70°0'0"E95°0'0"E [...] 9 m grown thermodynamically (mode) dynamic processes doubled the ice thickness to 1.94 m 4 Haas, Sea Ice Book, 2017 Thermodynamic growth Modelled thickness 0.95 m Level and deformed ice 5 10 15 20 km z

3DVar-FGAT with 5-day window (in-situ profiles, sea-level anomalies, SST) – Sea ice univariate 3DVar-FGAT of sea-ice concentration from passive microwave • Arctic sea-ice forecasts more skilful (Zampieri 2018) [...] seasonal sea-ice cover more important for sub-seasonal predictions ORAS5 – CS2SMOS Average sea-ice thickness in March 2011-2015 m m October 29, 2014 Initializing sub-seasonal forecasts with CS2SMOS sea-ice [...] applied from November to March • Impact of sea-ice thickness on sea-ice concentration largest in melt season 7 October 29, 2014 Impact of CS2SMOS initialization on sea ice forecasts: March - June 8 Spatially

navigation in ice infested waters ● Retreating sea ice in the Arctic gives a potential for commercial ship traffic at high latitudes ● Northern sea route from North Sea ports to the Far East is ~40% shorther than [...] assimilated product: OSISAF sea ice 9th IICWG-DA, Bremen 2019-06-17 - 199th IICWG-DA, Bremen 2019-06-17 - 19 OSISAF vs ice charts Impact of assimilated product: OSISAF sea ice 9th IICWG-DA, Bremen 2019-06-17 [...] Displacement between ice edge grid cells in model results and observations 2. Area of mismatching sea ice extent in model results and observations 3. Ice edge position skill as a function of horizontal

primarily been used to map the land-sea mask of the MPIOM to ECHAM5 (H. Haak, per- sonal communication). 2.2 The Finite Element Sea Ice-Ocean Model (FESOM) The sea ice-ocean component in the coupled system [...] primarily been used to map the land-sea mask of the MPIOM to ECHAM5 (H. Haak, per- sonal communication). 2.2 The Finite Element Sea Ice-Ocean Model (FESOM) The sea ice-ocean component in the coupled system [...] primarily been used to map the land-sea mask of the MPIOM to ECHAM5 (H. Haak, per- sonal communication). 2.2 The Finite Element Sea Ice-Ocean Model (FESOM) The sea ice-ocean component in the coupled system

Antarctica, Siberia and North America. Removes singularities on the ocean points Resolution roughly around 0.25 degree globally 4 European Centre for Medium-Range Weather Forecasts Coupling sea ice in the IFS Coupling [...] a dynamic-thermodynamic sea ice model Implementation both at medium and monthly to seasonal timescales have been largely neutral for the large scale circulation The dynamic sea ice model does allow the [...] PowerPoint Presentation NWP with a thermodynamic-dynamic sea ice model Sarah Keeley & Kristian Mogensen Coupled Processes Team, ECMWF © ECMWF June 24, 2019 Persistence – not a good assumption, especially

) ● SIV over West Laptev Sea region highly sensitive to same variables, but mainly in region 6 17eolab.dk y = N(x) σ2(y) = N' C(x) N'T + σ2(y mod ) ● SIV over East Siberian Sea region highly sensitive to [...] state, ● forcing, ● process parameters y: target quantity ● Could be anything that is modeled ● Here: sea ice volume and snow volume Quantitative Network Design: 2-Step Procedure Some state variable of the [...] ) σ(y 0 ) x: control vector ● initial state, ● forcing, ● process parameters y: target quantity ● sea ice volume ● snow volume d = M(x) C(x) = (M'T C(d)-1 M' + C(x 0 )-1)-1 5eolab.dk time C(x) C(d 2 )