The decomposition of geophysical processes into relevant modes is a key issue for characterization, forecasting and reconstruction problems. The blind separation of contributions from different sources is a well-studied problem in signal and image processing. Recently, significant advances have been reported with the introduction of non-negative and sparse formulations. In this work, we address an extension to the blind decomposition of linear operators or transfer functions between variables of interest with an emphasis on a non-negative setting. As illustrated here, such decompositions are of key interest for the analysis of geophysical dynamics and the relationships between different geophysical variables.