The Science of Seasonal Weather Prediction
Exploring how scientists use the INM RAS climate model to forecast seasonal weather anomalies months in advance
Imagine knowing months in advance whether next winter will be unusually harsh or surprisingly mild. While we've grown accustomed to reliable daily weather forecasts, predicting weather patterns weeks or months into the future represents one of meteorology's greatest challenges.
This isn't about knowing whether it will rain on a specific day three months from now, but rather forecasting large-scale climate anomalies—will temperatures be above or below normal? Will precipitation be excessive or scarce?
At the forefront of this scientific frontier are climate models like the one developed at the Institute of Numerical Mathematics of the Russian Academy of Sciences (INM RAS), whose researchers are pushing the boundaries of what's predictable in our complex climate system.
Forecasts for entire seasons rather than specific days
Predicting deviations from long-term averages
Sophisticated computer simulations of Earth's systems
Climate models are sophisticated computer programs that simulate the Earth's climate system by representing the physical processes of the atmosphere, oceans, land surface, and ice. Unlike weather forecast models that predict specific conditions over days, climate models focus on longer-term trends and statistical probabilities.
The INM RAS climate model is a comprehensive system that evolves through various versions, with each iteration incorporating improved representations of atmospheric processes, higher resolution, and better physical parameterizations 9 .
Seasonal climate predictability arises largely from slower-varying components of the climate system that influence atmospheric behavior:
Earlier generation model with foundational climate simulation capabilities
Significant improvements including higher vertical resolution, added aerosol block, and modified cloud parameterization 9
Continued refinement with enhanced physical processes and computational efficiency
In their 2021 study, Vorobyeva and Volodin designed an innovative experiment to test the seasonal predictability of weather using the INM RAS climate model 1 5 .
They utilized the INMCM5.0 model, which features improvements including higher vertical resolution in the stratosphere, raised upper boundary, added aerosol block, and modified parameterization of clouds and condensation 9 .
| Component | Function | Role in Seasonal Prediction |
|---|---|---|
| Atmospheric Model | Simulates circulation, temperature, precipitation | Core component for weather pattern prediction |
| Ocean Model | Represents ocean currents, temperatures, and heat storage | Crucial for modeling ENSO effects |
| Soil-Snow Module | Handles land surface processes, snow accumulation and melt | Important for surface energy balance |
| Aerosol Module | Simulates atmospheric particles and their effects | Enhances physical realism of simulations |
| Supercomputing Infrastructure | Processes enormous computational demands | Enables high-resolution, ensemble simulations |
The experiment yielded several significant findings that advance our understanding of seasonal predictability:
| Meteorological Variable | Predictive Skill | Notable Regional Variations |
|---|---|---|
| Sea-Level Pressure | Moderate to High | Highest in tropical regions |
| Surface Temperature | Moderate | Improved during ENSO events |
| Precipitation | Lower | Most challenging to predict |
One notable advancement in the INM RAS model has been the refinement of how it represents snow cover. The modified snow module now accounts for liquid water retention and refreezing within the snow layer—particularly important during transitional seasons when surface temperatures fluctuate around 0°C .
This process affects snow density and melt timing, which in turn influences surface albedo and temperature patterns, ultimately improving the model's ability to simulate climate anomalies, especially in middle and high latitudes .
Implementing these sophisticated physical representations requires tremendous computational resources. Researchers working with the INM RAS model have identified and addressed significant bottlenecks in data input and output operations 6 .
Through optimization of data gathering routines, they achieved remarkable reductions in processing time—by factors of 102-103 on some supercomputing systems 6 . These efficiency gains enable more complex experiments and higher-resolution simulations that were previously computationally prohibitive.
| System Component | Initial Performance | After Optimization | Improvement Factor |
|---|---|---|---|
| Data Gathering Time | Significant bottleneck | Greatly reduced | 102-103 times faster |
| Overall Model Running Time | Limited by output operations | Substantially improved | Varies by system |
| Scalability | Constrained | Enhanced | Enables higher-resolution runs |
The experimental studies conducted with the INM RAS climate model represent significant strides in the challenging field of seasonal weather predictability. While perfect prediction of specific daily weather months in advance remains beyond reach, the demonstrated ability to forecast seasonal anomalies—particularly during strong climate forcing events like El Niño and La Niña—offers substantial value to numerous sectors including agriculture, water resource management, and disaster preparedness 1 5 .
As climate models continue to evolve, incorporating more realistic representations of physical processes like snow metamorphism , and as computational capabilities advance 6 , we move closer to reliable seasonal outlooks that can inform decision-making months in advance.
The seamless prediction problem—bridging the gap between weather and climate forecasting—remains an active and vital area of research that promises practical benefits for society as we adapt to an increasingly variable climate.
Seasonal forecasts help farmers plan planting and harvesting, optimize irrigation, and prepare for extreme weather events.
Predicting seasonal precipitation patterns assists in reservoir management, flood control, and drought preparedness.
1 Vorobyeva, V. & Volodin, E. (2021). Experimental studies of seasonal predictability based on the INM RAS climate model.
3 Reference to SLAV model comparison study.
5 Vorobyeva, V. & Volodin, E. (2021). Additional details on seasonal predictability experiments.
6 Study on computational optimizations for the INM RAS model.
9 Documentation on INM RAS climate model versions and improvements.
Research on snow parameterization improvements in the INM RAS model.