Statistical-Induced Uncertainties in Climate Modeling: Challenges and the Imperative of Physical Process-Oriented Closure

Abstract

This paper examines advancements in climate modeling, emphasizing integrated, physics-grounded and process-oriented approaches to enhance predictive reliability. It underscores the critical role of multiphase phenomena in atmospheric systems, including interfacial heat and mass transfers, and the integration of empirical data and high-resolution observational networks. Coupled with targeted laboratory and numerical experiments, these elements refine the physical basis of climate models. Efforts focus on addressing model limitations, including feedback uncertainties and challenges in AI/ML integration. A central focus is placed on “Statistical-Induced Uncertainties” (systemic biases introduced by spatio-temporal averaging, data interpolation, and ensemble processing) which propagate across modeling stages and may obscure physical interpretations. By embedding empirical rigor and prioritizing transparency, the study advocates for interdisciplinary collaboration to fill observational gaps, especially in under-observed regions, with statistical approaches aligned with physical interpretability. The paper highlights the value of ensemble modeling and AI, not as substitutes but as complements to physics-driven frameworks, supported by clear interpretive methods that anchor models in fundamental process closure. This integrated approach is essential for advancing climate projections and informing effective responses to global climate challenges.