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Abstract: This paper shows that maximum snow depth and the length of accumulation and ablation periods observed at the local scale of Hornsund, SW Spitsbergen, are partly explained by monthly and seasonal values of the AO and NAO indices in the given and previous hydrological years. This analysis is followed by an application of a statistically efficient lumped parameter time series approach to modelling the dynamics of snow depth, based on daily meteorological and snow depth measurements from the same area. A dynamic Stochastic Transfer Function (STF) model is developed that follows the Data Based Mechanistic approach, where a stochastic data-based identification of model structure and an estimation of its parameters are followed by a physical interpretation. Apart from snow depth estimates, the model provides also the uncertainty limits. An analysis of the variation in parameter estimates over the whole measurement period provides an insight into the possible influence of recent climate change on snow cover dynamics at Hornsund. To help explain the physical meaning of the model parameters, we classified the data into accumulation and ablation periods. The models were run for each period separately. The first order model structure was found to be the most suitable to explain the variability of the snow cover. The cross-validation of models performance on the other years shows that the predictive value of the obtained models is not very consistent, with a mixture of good and bad years. The analysis shows that variability in the NAO and AO indices, reflecting the changes in global circulation patterns, is reproduced by local, physically meaningful, STF model-derived parameters in the form of residence times and temperature and precipitation related gains. [Copyright &y& Elsevier]