AI-Driven Climate Modeling: Validation and Uncertainty Mapping – Methodologies and Challenges
DOI:
https://doi.org/10.62486/latia2025332Keywords:
Climate Modeling, validation methodologies, uncertainties methodologies, challenges, climate systemsAbstract
Climate models are fundamental for predicting future climate conditions and guiding mitigation and adaptation strategies. This study aims to enhance the accuracy and reliability of climate modeling by integrating artificial intelligence (AI) techniques for validation and uncertainty mapping. AI-driven approaches, including machine learning-based parameterization, ensemble simulations, and probabilistic modeling, offer improvements in model precision, quality assurance, and uncertainty quantification. A systematic review methodology was applied, selecting peer-reviewed studies from 2000 to 2023 that focused on climate modeling, validation, and uncertainty estimation. Data sources included observational records, satellite measurements, and global reanalysis datasets. The study analyzed key AI-driven methodologies used for improving model accuracy, including statistical downscaling techniques and deep learning-based uncertainty prediction frameworks. Findings indicate that AI-enhanced models significantly improve climate projections by refining parameterization, enhancing bias correction, and optimizing uncertainty quantification. Machine learning applications facilitate more accurate predictions of meteorological phenomena, including temperature and precipitation variability. However, challenges remain in addressing observational biases, inter-model inconsistencies, and computational limitations. The study concludes that AI-driven advancements provide critical improvements in climate model reliability, yet ongoing refinements are necessary to address persistent uncertainties. Enhancing observational datasets, refining computational techniques, and strengthening model validation frameworks will be essential for reducing uncertainty. Effective communication of climate model outputs, including uncertainty mapping, is crucial for supporting informed policy decisions. AI-driven climate modeling is a rapidly evolving field, and continuous innovation will be key to improving predictive accuracy and resilience in climate adaptation strategies.
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