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Environmental and climate processes are often distributed over largespace-time domains. Their complexity and the amount of available data makemodelling and analysis a challenging task. Statistical modelling of environmentand climate data can have several different motivations includinginterpretation or characterisation of the data. Results from statisticalanalysis are often used as a integral part of larger environmental studies.Spatial statistics is an active and modern statistical field, concerned withthe quantitative analysis of spatial data; their dependencies anduncertainties. Spatio-temporal statistics extends spatial statistics throughthe addition of time to the, two or three, spatial dimensions. The focus ofthis introductory paper is to provide an overview of spatial methods and theirapplication to environmental and climate data. This paper also gives anoverview of several important topics including large data sets andnon-stationary covariance structures. Further, it is discussed how Bayesianhierarchical models can provide a flexible way of constructing models.Hierarchical models may seem to be a good solution, but they have challenges oftheir own such as, parameter estimation. Finally, the application ofspatio-temporal models to the LANDCLIM data (LAND cover - CLIMate interactionsin NW Europe during the Holocene) will be discussed.

Spatial methods and their applications to environmental and climate data