Effective Utilisation of AI to Improve Global Warming Mitigation Strategies through Predictive Climate Modelling

Abstract

The application of Artificial Intelligence (AI) in climate prediction models significantly enhances the accuracy and efficiency of climate forecasts, addressing the limitations of conventional models. Traditional models, such as General Circulation Models (GCMs), rely on deterministic algorithms and historical data, often struggling with processing inefficiencies and inaccuracies due to their inability to handle dynamic environmental variables in real time. While GCMs produce reliable simulations grounded in physical laws, they demand substantial computational power and lack adaptability, which can lead to errors, especially in long-term climate projections. In contrast, AI-driven models leverage machine learning, particularly deep learning and neural networks, to analyse large, complex datasets like satellite imagery, ocean currents, and atmospheric variables. These models employ adaptive learning techniques, allowing for continuous recalibration and improvement as new data becomes available, ensuring more precise and timely forecasts. Compared to GCMs, AI models have demonstrated faster processing speeds and enhanced scalability despite being computationally intensive during training. AI-based models have shown significant improvements in prediction accuracy, particularly in regional climate modelling and short- to medium-term forecasts. In comparative studies, these models exhibited a 20–30% increase in prediction accuracy and a 50% reduction in processing time. However, challenges such as the need for large, high-quality datasets and the risk of overfitting persist, potentially affecting model generalizability. Nevertheless, AI models offer notable advancements in real-time climate monitoring and decision-making for global warming mitigation strategies.