Machine Learning Climate Model Uncertainty Quantification

Develop a scikit-learn and TensorFlow hybrid framework for probabilistic uncertainty estimation in climate prediction models. Create a modular system that can ingest multi-dimensional climate datasets, implement Bayesian neural network architectures, and generate comprehensive uncertainty intervals for temperature and precipitation forecasts. The solution must support dynamic model retraining, provide visualizations of prediction confidence, and be compatible with climate research publication standards.