From a machine learning, and data architecture standpoints, the process of turning climate science into policy resembles a classical pipeline: raw data intake, feature engineering, deterministic modeling, and final product generation. Nevertheless, in contrast to conventional machine learning on tabular data, computational climatology raises issues like irregular spatial-temporal scales, non-linea...
This analysis presents a constructive framework for translating climate data into policy-relevant insights, emphasizing the often-overlooked data engineering challenges in climate science. The pipeline’s strength lies in its pragmatic integration of domain-specific models—climatology, epidemiology, and economics—to produce actionable metrics like excess mortality and economic losses. By using percentile-based thresholds and wet-bulb temperature, it avoids the pitfalls of one-size-fits-all global...