Climate Models: Evolution, Working & Challenges

• At the UN General Assembly, the US President criticized global climate forecasts as unreliable. However, empirical evidence confirms that climate models are crucial for predicting long-term climate trends.

What are Climate Models?

• Definition: Computer simulations that use physics, chemistry, and biology to replicate interactions among the atmosphere, oceans, land, and ice.
• Purpose: Predict long-term climate trends under varying emission and land-use scenarios.
• Difference from Weather Models: Weather models predict short-term local conditions, while climate models project long-term global patterns.

Evolution of Climate Models

• Energy Balance Models: Focused on Earth’s surface temperature based on the balance between incoming solar and outgoing thermal radiation (1960s)
• Radiative Convective Models: Added vertical energy transfer in the atmosphere to estimate temperature variation with altitude. (1970s)
• General Circulation Models: Simulate the full climate system, atmosphere, oceans, land, and ice, capturing large-scale processes globally. (1980s–Present)
• Earth System Models: Integrated biogeochemical cycles (carbon, nitrogen) to study feedbacks between climate and ecosystems. (1990s–Present)
• Regional Climate Models: Downscaled versions of global models, providing higher-resolution, localised projections for specific regions. (2000s–Present)
• AI-Driven Climate Models: Use machine learning and neural networks (e.g., DeepMind GraphCast, NVIDIA Earth-2) for faster and more precise forecasting. (2020s–Future).

How Climate Models Work

• 3D Grid System: Earth is divided into cells horizontally and vertically, representing land, air, and ocean.
• Mathematical Equations: Each cell simulates the flow of gases and heat between neighbouring cells.
• Inputs: Real-world data like CO₂ levels, ocean currents, and solar radiation.
• Outputs: Projections on temperature, precipitation, sea-level rise, and extreme weather frequency.

Foundation Courses

Role of Climate Models in Climate Resilience

• Long-Term Forecasting: Models predict global temperature may rise 1.5–2°C by 2040 (IPCC 2023).
• Prediction: CORDEX South Asia helped Kerala & Assam prepare for 2023 floods, reducing damage.
• Agricultural: Supports over 140 million farmers under PM-Kisan with crop & irrigation forecasts.
• Infrastructure: Tamil Nadu built cyclone-proof coastal embankments using regional climate projections.
• Policy Support: Model data helps implement climate action plans in over 200 Indian districts.

Challenges Faced by Climate Models

• Data Gaps: Limited Observations due to Sparse monitoring networks in developing regions.
• Model Bias: North-Centric Models calibrated mainly with North American & European datasets.
• Resolution Constraints: Grid cells are too large to capture urban heat islands or rural flooding.
• Complex Interactions: Uncertainty in how aerosols, soil moisture, and vegetation affect climate.

Way Forward

• Data Democratisation: Expand ground-based climate data through joint initiatives. E.g., Global Framework for Climate Services (GFCS) under WMO.
• Model Localisation: Develop India and Asia-specific models for monsoon accuracy. E.g., CORDEX South Asia under IPCC’s project.
• AI Integration: Use AI to enhance pattern detection and reduce uncertainty. E.g., Google DeepMind’s GraphCast outperforms traditional weather models.
• Capacity Building: Invest in research collaboration and supercomputing for regional institutes. E.g., India’s National Supercomputing Mission for improved forecasting.

Recent extreme events like Kerala & Assam floods show that downscaled, AI-driven climate models are crucial for accurate monsoon forecasting, disaster preparedness, & localized climate resilience planning.

Reference: The Hindu | PMFIAS: Climate Predictions

PMF IAS Pathfinder for Mains – Question 374

Q. India’s environmental planning heavily depends on regional climate projections. Examine how downscaled climate models can enhance climate resilience and disaster preparedness at the local level. (150 Words) (10 Marks)

Approach

• Introduction: Write a brief introduction by mentioning a current example.
• Body: Examine the role of downscaled climate models and the way forward.
• Conclusion: Emphasis on climate resilience to enhance climate resilience and disaster preparedness at the local level.