  Spatial data collected worldwide at a huge number of locations are frequently
used in environmental and climate studies. Spatial modelling for this type of
data presents both methodological and computational challenges. In this work we
illustrate a computationally efficient non parametric framework to model and
estimate the spatial field while accounting for geodesic distances between
locations. The spatial field is modelled via penalized splines (P-splines)
using intrinsic Gaussian Markov Random Field (GMRF) priors for the spline
coefficients. The key idea is to use the sphere as a surrogate for the Globe,
then build the basis of B-spline functions on a geodesic grid system. The basis
matrix is sparse and so is the precision matrix of the GMRF prior, thus
computational efficiency is gained by construction. We illustrate the approach
on a real climate study, where the goal is to identify the Intertropical
Convergence Zone using high-resolution remote sensing data.
