Finn Lindgren,
Mathematical Statistics,
Lund University

Title: 
A stochastic modelling perspective on global temperature reconstruction

Abstract: Analysis of regional and global mean temperatures based on instrumental observations has typically been based on aggregating temperature measurements to grid cells (Brohan et al., 2006). A potential deficiency in the uncertainty estimates for such gridded data is that the spatial statistical dependence between temperature anomalies is partly ignored, in particular with respect to effects of the spherical geometry of the earth. From a modelling perspective, a more natural approach is to construct a spatial stochastic model for the temperature field directly on the globe, together with a model for the measurements, without aggregation. Such a model can incorporate non-stationary spatial dependencies due to differences between the tropical and polar regions, as well as local covariate information such as topography. Local and global yearly temperature means are then computed along with uncertainty estimates using a Bayesian integration scheme. Extending the model with temporally changing parameters could give a statistically sound basis for uncertainties in observed climate change. Since the PhD thesis by Das (2000), recent advances in methods for non-stationary stochastic fields have made the needed computations much more tractable. In this talk, some preliminary results along this direction will be presented.