[
    {
        "id": "osp-15314",
        "type": "article-journal",
        "title": "Artificial intelligence pathways from weather to climate",
        "author": [
            {
                "family": "Beucler",
                "given": "Tom"
            },
            {
                "family": "Neelin",
                "given": "J. David"
            },
            {
                "family": "Su",
                "given": "Hui"
            },
            {
                "family": "Asthana",
                "given": "Shivanshi"
            },
            {
                "family": "Bretherton",
                "given": "Chris"
            },
            {
                "family": "Chapman",
                "given": "Will"
            },
            {
                "family": "Christopoulos",
                "given": "Costa"
            },
            {
                "family": "Clark",
                "given": "Spencer K."
            },
            {
                "family": "Grover",
                "given": "Aditya"
            },
            {
                "family": "Lopez-Gomez",
                "given": "Ignacio"
            },
            {
                "family": "Schneider",
                "given": "Tapio"
            },
            {
                "family": "Subel",
                "given": "Adam"
            },
            {
                "family": "Watt-Meyer",
                "given": "Oliver"
            }
        ],
        "URL": "https://omanscience.com/ar/articles/artificial-intelligence-pathways-from-weather-to-climate",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Deep learning has made rapid advances in weather forecasting: autoregressive models trained on atmospheric reanalyses now rival dynamical models across nowcasting, medium-range, and subseasonal-to-seasonal lead times, producing well-calibrated ensemble forecasts at reduced cost. We review these advances and consider their extension to climate horizons, where the challenge shifts from initial-condition skill to producing reliable statistical responses under altered forcings. AI-powered climate prediction systems must produce credible forced responses to drivers (e.g., greenhouse gases, land-use change) typically outside the observed record. We propose two minimum requirements for AI in climate modeling: (i) external forcing agents must enter explicitly enough to support interventions in which they vary independently; and (ii) robustness must be stress-tested in out-of-distribution regimes, including extremes and counterfactual trajectories. Using leading AI autoregressive emulators and hybrid physics-AI models, we identify development and coupling challenges. Comparing the reported throughput of these models with that of GPU-ported dynamical models highlights how AI can reduce time-to-solution by advancing only the target variables at the required resolution and using longer time steps, rather than integrating a full high-frequency, multivariate state. Diverse AI downscaling strategies can partially substitute for explicit fine-scale resolution, paving the way toward inexpensive local hazard assessment across prediction horizons."
    }
]