  This paper highlights methods from geostatistics that are relevant to the
interpretation, intercomparison, and synthesis of atmospheric model data, with
a specific application to exoplanet atmospheric modeling. Climate models are
increasingly used to study theoretical and observational properties of
exoplanets, which include a hierarchy of models ranging from fast and idealized
models to those that are slower but more comprehensive. Exploring large
parameter spaces with computationally-expensive models can be accomplished with
sparse sampling techniques, but analyzing such sparse samples can pose
challenges for conventional interpolation functions. Ordinary kriging is a
statistical method for describing the spatial distribution of a data set in
terms of the variogram function, which can be used to interpolate sparse
samples across any number of dimensions. Variograms themselves may also be
useful diagnostic tools for describing the spatial distribution of model data
in exoplanet atmospheric model intercomparison projects. Universal kriging is
another method that can synthesize data calculated by models of different
complexity, which can be used to combine sparse samples of data from slow
models with larger samples of data from fast models. Ordinary and universal
kriging can also provide a way to synthesize model predictions with sparse
samples of exoplanet observations and may have other applications in exoplanet
science.
