A Foundational Framework and Benchmarking Methodology for Observer-Dependent Entropy Retrieval in Climate Science

Climate forecasting models typically assume homogeneous information processing across all stakeholders, neglecting critical real-world retrieval asymmetries. We introduce Observer-Dependent Entropy Retrieval (ODER), a mathematically rigorous framework that redefines climate forecasting not solely as a predictive exercise but as an observer-dependent retrieval process, reframing uncertainty and risk as functions of latency, hierarchy, and actor-specific access. Using a Bayesian–Markovian formulation, ODER provides a structure for incorpo- rating observer-specific entropy retrieval into forecasting. This paper presents a foundational framework with conceptual demonstrations and proposed validation pathways. We outline a benchmarking methodology and define proxies for empirical calibration that could connect theory to practice. ODER transforms climate forecasting by making retrievability—not just predictive accuracy—central to early warning and decision-making.