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While most spatial data can be modeled with the assumption that distantpoints are uncorrelated, some problems require dependence at both far and shortdistances. We introduce a model to directly incorporate dependence in phenomenathat influence a distant response. Spatial climate problems often have suchmodeling needs as data are influenced by local factors in addition to remotephenomena, known as teleconnections. Teleconnections arise from complexinteractions between the atmosphere and ocean, of which the El Nino--SouthernOscillation teleconnection is a well-known example. Our model extends thestandard geostatistical modeling framework to account for effects of covariatesobserved on a spatially remote domain. We frame our model as an extension ofspatially varying coefficient models. Connections to existing methods arehighlighted and further modeling needs are addressed by additionally drawing onspatial basis functions and predictive processes. Notably, our approach allowsusers to model teleconnected data without pre-specifying teleconnectionindices, which other methods often require. We adopt a hierarchical Bayesianframework to conduct inference and make predictions. The method is demonstratedby predicting precipitation in Colorado while accounting for local factors andteleconnection effects with Pacific Ocean sea surface temperatures. We show howthe proposed model improves upon standard methods for estimating teleconnectioneffects and discuss its utility for climate applications.

Remote effects spatial process models for modeling teleconnections