[1] The ability to simulate the past variability of the sea ice-ocean system is of fundamental interest for the identification of key processes and the evaluation of scenarios of future developments. To achieve this goal atmospheric surface fields are reconstructed by statistical means for the period 1900 to 1997 and applied to a coupled sea ice-ocean model of the North Atlantic/Arctic Ocean. We devised a statistical model using a redundancy analysis to reconstruct the atmospheric fields. Several sets of predictor and predictand fields are used for reconstructions on different time scales. The predictor fields are instrumental records available as gridded or station data sets of sea level pressure and surface air temperature. The predictands are surface fields from the NCAR/NCEP reanalysis. Spatial patterns are selected by maximizing predictand variance during a ``learning'' period. The reliability of these patterns is tested in a validation period. The ensemble of reconstructions is checked for robustness by mutual comparison and an ``optimal'' reconstruction is selected. Results of the simulations with the sea ice-ocean model are compared with historical sea ice extent observations for the Arctic and Nordic Seas. The results obtained with the ``optimal'' reconstruction are shown to be highly consistent with these historical data. An analysis of simulated trends of the ``early 20th century warming'' and the recent warming in the Arctic complete the manuscript.