# Machine Learning Methods in Weather and Climate Applications: A Survey
## 6 Medium-to-Long-Term Climate Prediction
### Model Design  
**Climate Model. ** Climate models, consisting of fundamental atmospheric dynamics and thermodynamic equations, focus on simulating Earth's long-term climate system [89]. Unlike NWP, which targets short-term weather patterns, climate models address broader climatic trends. These models encompass Global Climate Models (GCMs), which provide a global perspective but often at a lower resolution, and Regional Climate Models (RCMs), designed for detailed regional analysis [90]. The main emphasis is on the average state and variations rather than transient weather events. The workflow of climate modeling begins with initialization by setting boundary conditions, possibly involving centuries of historical data. Numerical integration follows, using the basic equations to model the long-term evolution of the climate system [91]. Parameterization techniques are employed to represent sub-grid-scale processes like cloud formation and vegetation feedback. The model's performance and uncertainties are then analyzed and validated by comparing them with observational data or other model results [92]. The advantages of climate models lie in their ability to simulate complex climate systems, providing forecasts and insights into future climate changes, thereby informing policy and adaptation strategies. However, they also present challenges such as high computational demands, sensitivity to boundary conditions, and potential uncertainties introduced through parameterization schemes. The distinction between GCMs and RCMs and their integration in understanding both global and regional climate phenomena underscores the sophistication and indispensable role of these models in advancing meteorological studies [93]. **Conditional Generative Forecasting [61]. ** In the intricate arena of medium-to-long-term seasonal climate prediction, the scarcity of substantial datasets since 1979 poses a significant constraint on the rigorous training of complex models like CNNs, thus limiting their predictive efficacy. To navigate this challenge, a pioneering approach to transfer learning has been embraced, leveraging the simulated climate data drawn from CMIP5 (Coupled Model Intercomparison Project Phase 5) [94] to enhance modeling efficiency and accuracy. The process begins with a pre-training phase, where the CNN is enriched with CMIP5 \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Forecast-Timeliness** & **Z500 RMSE (7 Days)** & **Z500 ACC (7 Days)** & **Training- Complexity** & **Forecasting-Speed** \\ \hline MetNet [46] & 8 h & - & - & 256 Google-TPU- accelerators (16-days-training) & Fewer seconds \\ \hline FourCastNet [47] & 7 days & 595 & 0.762 & 4 A100-GPU & 24-h forecast for 100 members in 7 s \\ \hline GraphCast [48] & 9.75 days & 460 & 0.825 & 32 Cloud-TPU-V4 (21-days-training) & 10-days-prediction within 1 min \\ \hline PanGu [49] & 7 days & 510 & 0.872 & 192 V100-GPU (16-days-training) & 24-h-global- prediction in 1.4 s for each GPU \\ \hline IPS [88] & 8.5 days & 439 & 0.85 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Short-term weather forecast model result comparison. data to comprehend essential climatic patterns and relationships. This foundational insight then transfers seamlessly to observational data without resetting the model parameters, ensuring a continuous learning trajectory that marries simulated wisdom with empirical climate dynamics. The methodology culminates in a fine-tuning phase, during which the model undergoes subtle refinements to align more closely with the real-world intricacies of medium-to-long-term ENSO forecasting [18]. This innovative strategy demonstrates the transformative power of transfer learning in addressing the formidable challenges associated with limited sample sizes in medium-to-long-term climate science.