AN INTEGRATED ARTIFICIAL INTELLIGENCE AND INFORMATION TECHNOLOGY FRAMEWORK FOR CLIMATE MODELING AND SUSTAINABILITY DECISION OPTIMIZATION

Abstract

The escalating climate crisis demands a radical paradigm shift to extend beyond traditional modeling methods that still suffer due to computational constraints, parameterization gaps as well as the disparity between global modeling and the local decision-making levels. To address these important gaps, the paper demonstrates a transformative Artificial Intelligence and Information Technology framework which combines hybrid physics-AI systems, generative machine learning and policy optimization tools. Using a combination of systematic testing of Neural GCM, dynamical-generative downscaling, physics-constrained neural networks, and analysis of emissions using machine learning, we show new capabilities in climate science never before seen. Physics-AI hybrids reduce errors in precipitation forecasts by 40% but offer 372× computational speeds as well as significantly better representations of extreme precipitation events which previously have been afflicted by systematic drizzle bias. The model of generative diffusion can be used to downscale images at high resolution (greater than 800 samples per hour) and maintain multivariate correlations needed to measure extreme events in compounds- a feature formerly impractical to compute with purely dynamical models. The analysis of the emissions data of 195 countries (1900-2023) that is based on the machine learning reveals carbon intensity of economic activity as the most significant predictive feature (78.0% importance), accompanied by empirically-based national typologies that necessitate differentiated policy interventions, but not the adopted one-size-fits-all measures. More importantly, transfer learning has shown that AI models that are trained under historical conditions can be adapted to new climatic conditions (4×CO2) with only 1% of new training data, which is the root cause of the generalization issue of AI usage in climate science. There is also the operational viability of the framework which is based on proven case studies of monsoon forecasting, infrastructure planning in deep uncertainty, and agricultural decision support systems. This combination of artificial intelligence and information technology with climate science is not just a case of incremental improvement but it is an overhaul of the human ability to comprehend, foresee, and act in response to the accelerating environmental change.

Keywords : Climate modeling, artificial intelligence, hybrid physics-AI systems, generative downscaling, machine learning, extreme events, policy analysis, transfer learning.