This paper addresses the challenge of detecting climate disinformation narratives by re-framing traditional detection as a retrieval task. Rather than relying on fixed taxonomies that cannot accommodate emerging narratives, the approach ranks texts by alignment with a given narrative core message. The proposed SpecFi framework generates hypothetical documents to bridge abstract narrative descriptions with concrete textual instantiations, using community summaries from graph-based detection as few-shot examples for generation. The method achieves a MAP of 0.505 on the CARDS dataset without accessing narrative labels. Additionally, the paper introduces narrative variance, an embedding-based difficulty metric, and demonstrates its utility through partial correlation analysis for understanding narrative complexity in climate disinformation detection.