Open-Meteo Climate & Ensemble MCP. Model long-term regional climate scenarios with IPCC data.

Open-Meteo Climate & Ensemble gives your AI client direct access to IPCC-grade climate simulations and probabilistic forecasting data. You run multi-model ensemble forecasts, track long-term temperature trajectories across specific regions, and project precipitation under varied Shared Socioeconomic Pathway (SSP) emission scenarios (2015–2100). This is the core data set you need for quantifying systemic environmental risk in ESG reporting or planning infrastructure.**

Projecting Long-Term Climate Scenarios: You use get_climate_projection to run CMIP6 models. This tool estimates climate change projections, giving you detailed temperature and precipitation forecasts spanning from 2015 through 2100. It lets you assess risk under defined SSP emission scenarios, predicting how the environment changes over decades based on different paths of global emissions.

You can analyze both temperature shifts and rainfall patterns simultaneously for specific locations.

Calculating Multi-Model Uncertainty Ranges: To understand the range of possible outcomes, you use get_ensemble_forecast. This tool generates probabilistic forecasts by aggregating data from multiple distinct weather models. It doesn't give you one number; it gives you a range, which quantifies uncertainty across immediate climate predictions. When single-model outputs aren't enough for risk assessment—and they usually aren't—the ensemble approach defines the full spectrum of potential outcomes.

Analyzing Historical Temperature Trends: You run get_climate_temperature_trend to determine a long-term trajectory analysis of average temperatures. This function calculates how average temperatures change over time for any specified geographic region, allowing you to track climate shifts against established historical norms. It gives you the decades-long view needed to spot clear warming or cooling trends.

How You Use the Data: The server structures this data so your agent gets context, not just isolated numbers. You can compare current conditions directly against projected ranges derived from CMIP6 models. You'll evaluate the full spread of predictions when comparing results across the ensemble forecasts. This mechanism is essential when you're advising stakeholders who need to know not just what will happen—a specific temperature or rain amount—but precisely how certain that prediction is based on multiple scientific models.

The combination of get_climate_projection and get_ensemble_forecast lets your agent model the risk space, providing a much richer picture than any single dataset could offer.