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Summary form only given. This special session has been motivated by the growing importance of data-driven modeling in Earth sciences, climate modeling, meteorological and oceanographic applications, geophysical data processing, and hydrology. Of particular interest are the methodological aspects of learning methods, with the clarification of the advantages and limitations of learning techniques in the context of specific applications. This panel includes informal presentations by the session co-chairs followed by questions and answers from the audience. Topics of discussion include the following: 1) to identify major types of problems encountered in this field; 2) how to estimate the quality of data-driven models; 3) what are specific characteristics of data sets in climate modeling/Earth sciences that make them different from other applications; and 4) try to come to an agreement on possible benchmark data sets in this field.