Machine Learning Pipeline for Climate Model Uncertainty Quantification

Architect an end-to-end machine learning pipeline specifically designed for quantifying uncertainty in complex climate models. Implement advanced ensemble techniques that can handle multi-dimensional geospatial datasets, integrate probabilistic error estimation, and generate interpretable uncertainty visualizations. The solution must support incremental learning and handle datasets with significant temporal and spatial heterogeneity.