Artificial Intelligence Applications in Climate Change Prediction

Abstract

Abstract Climate change presents a growing danger to ecosystems, infrastructure, and human safety, heightening the need for prediction systems that are both precise and rapid enough to facilitate prompt decision-making. Artificial intelligence (AI), especially deep learning, has arisen as a revolutionary enhancement to conventional physics-based numerical models. This manuscript examines the application of AI methodologies, such as graph neural networks, transformers, Fourier neural operators, and ensemble deep-learning frameworks, throughout the climate-prediction continuum, encompassing short-term weather forecasting to multi-decadal climate model simulation, sea-level forecasting, and greenhouse gas emission estimation. We encapsulate notable systems documented in contemporary literature, examine the computational and scientific benefits that AI presents in comparison to traditional numerical weather prediction (NWP), and delineate the key obstacles, such as data insufficiency, physical coherence, interpretability, and energy expenditure, that need resolution prior to the reliable implementation of AI-driven climate forecasting at an operational level. We finish with guidance on hybrid physics-AI modelling as a means to get dependable, practical climate insights.