Fram Strait sea ice export affected by thinning: comparing high-resolution simulations and observations
Variability and trends of Fram Strait sea ice area and volume exports are examined for the period of 1990–2010. Simulations from a high-resolution version of the MPIOM model (STORM project) reproduce area and volume export well when compared with NSIDC and ICESat satellite data and in-situ ice thickness observations. The fluxes derived from ice thickness and drift satellite products vary considerably, indicating a high uncertainty in these estimates which we mostly assign to the drift observations. The model captures the observed average seasonal cycles and interannual variability of ice export. The simulated mean annual sea ice area export is 860 × 103 km2 a− 1 (1990–2010), and the correlation with the NSIDC-based area fluxes is r = 0.67. The simulated mean annual volume export is 3.3 × 103 km3 a− 1 (1990–2010), close to the ICESat/ULS values, with a correlation of r = 0.58. The simulated monthly area export has a significant positive trend of + 10% per decade, explained by wind forcing. The major contribution to the robust trend in area export between June and September. Fram Strait ice volume export variability is mainly controlled by ice drift with a dominant role of the Transpolar Drift and, to a lesser extent thickness variability. The area export increase reflects increasing ice-drift speed, but is balanced with a reduced thickness over time when it comes to volume export, giving no significant trend in volume export. The spatial variability of ice drift indicates that the export influences a large area upstream in the Trans-Polar Drift stream, and that high volume export events lead to a thinner thickness there. The central Arctic is well connected drift-wise to the Fram Strait via the Transpolar Drift while for thickness, the region north of Greenland is dominated and controlled by the Fram Strait ice export.
KeywordsOcean-sea ice model Fram Strait Sea ice area export Sea ice volume export Sea ice export trends High-resolution model Arctic sea ice mass balance
We acknowledge the STORM consortium for ensuring the computational resources, and acknowledge AWI, CliSAP, MPI, HGZ for their financial support. We also acknowledge German Climate Computing Center (DKRZ) for their technical support, particularly regarding the code optimization. Through the provision observational data, our study was supported by the CORESAT project funded by the Norwegian Research Council (No. 222681). The AMSR-E and SSM/I data were provided by NSIDC (Boulder, USA). Lars H. Smedsrud was supported by the ice2ice project (ERC grant 610055) from the European Community’s Seventh Framework Programme (FP7/2007–2013). We thank Ron Kwok (Jet Propulsion Laboratory, USA), Gunnar Spreen (University of Bremen, Germany), for providing us the satellite data and Edmond Hansen (Norwegian Polar Institute, Norway) for giving us access to the ULS data. We also thank the two anonymous reviewers who provided helpful and constructive comments and suggestions to improve our manuscript.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
- Arakawa A, Lamb VR (1977) Computational design of the basic dynamical process of the UCLA general circulation model. Methods Comput Phys 17:173–265. https://doi.org/10.1016/B978-0-12-460817-7.50009-4 Google Scholar
- Comiso JC (2010) Variability and trends of the global sea ice cover. In: Thomas DN, Diekmann GS (eds) Sea ice, 2nd edn. Blackwell publishing, UK, pp 205–246Google Scholar
- EUMETSAT OSISAF (2010) Global sea ice concentration reprocessing dataset 1978–2007 (v1). Availabsssssle at: http://osisaf.met.no
- Gent PR, McWilliams JC (1990) Isopycnal mixing in ocean circulation models. J. Phys. Oceanogr. 20:150–160. https://doi.org/10.1175/1520-0485(1990)020%3C0150:IMIOCM%3E2.0.CO;2.CrossRefGoogle Scholar
- Ivanova N, Johannessen OM, Pedersen LT, Tonboe RT (2014) Retrieval of Arctic Sea ice parameters by satellite passive microwave sensors: a comparison of eleven sea ice concentration algorithms. IEEE Trans Geosci Remote Sens 52(11):7233–7246. https://doi.org/10.1109/TGRS.2014.2310136 CrossRefGoogle Scholar
- Kalnay E, et al. (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437–471. https://doi.org/10.1175/1520-0477(1996)077%3C0437:TNYRP%3E2.0.CO;2.CrossRefGoogle Scholar
- Maslowski W, Marble D, Walczowski W, Schauer U, Clement JL, Semtner AJ (2004) On climatological mass, heat, and salt transports through the Barents Sea and Fram Strait from a pan-Arctic coupled ice-ocean model simulation. J Geophys Res 109:C03032. https://doi.org/10.1029/2001JC001039 CrossRefGoogle Scholar
- Semtner AJ (1976) A model for the thermodynamic growth of sea ice in numerical investigations of climate. J Phys Oceanogr 6:379–389. https://doi.org/10.1175/1520-0485(1976)006%3C0379:AMFTTG%3E2.0.CO;2Oceanogr.CrossRefGoogle Scholar
- Tschudi M, Fowler C, Maslanik J, Stewart JS, Meier W (2016) Polar pathfinder daily 25 km EASE-grid sea ice motion vectors, Version 3. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://doi.org/10.5067/O57VAIT2AYYY
- Williams J, Tremblay B, Newton R: Dynamic preconditioning of the september sea-ice extent minimum. J Clim 29:5879–5891. https://doi.org/10.1175/JCLI-D-15-0515.1