Historical Context

Evolution of Climate Models

The evolution of climate models has been a critical aspect of understanding global environmental changes. In the general-circulation tradition, the Charney Report of 1979 provided one of the earliest rigorous estimates of equilibrium climate sensitivity at 1.5–4.5 °C for a doubling of CO₂, a range that held up remarkably well through decades of subsequent work^[22]. In the narrower domain of climate-impact studies for agriculture, early efforts relied heavily on statistical methods which were later complemented by process-based crop models such as DSSAT and APSIM^[6].

Early Statistical Models

The initial climate impact studies often used simple linear regression or similar techniques to forecast crop yields based on historical weather data.

Process-Based Modelling Era

With advancements in computer technology, more complex process-based models emerged. These simulate various components of the Earth system interactively and aim to provide a more realistic representation of the underlying physics than purely empirical fits^[7].

Recent Developments

The latest generation of climate models incorporates sophisticated cloud microphysics and other advanced features, though cloud feedbacks and convective parameterisation remain among the largest sources of uncertainty in long-range projections.

Challenges in Integrating AI into Climate Modelling

Data Quality Issues

Integrating artificial intelligence techniques requires high-quality, comprehensive datasets which are not always available. For example, historical records may lack sufficient detail for certain regions.

Model Calibration and Validation

A major challenge is ensuring that AI-enhanced models can be accurately calibrated and validated against real-world observations.