# Open-Meteo Climate & Ensemble MCP

> Open-Meteo Climate & Ensemble delivers IPCC-grade climate simulations and probabilistic forecasting directly to your AI client. It runs multi-model ensemble forecasts, tracks long-term temperature trajectories across specific regions, and projects precipitation under various SSP emission scenarios (2015–2100). This is the data backbone for quantifying systemic environmental risk in ESG reporting or infrastructure planning.

## Overview
- **Category:** the-unthinkable
- **Price:** Free
- **Tags:** climate-change, ipcc-projections, environmental-modeling, esg-reporting, probabilistic-forecasting

## Description

**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.

## Tools

### get_climate_projection
Runs CMIP6 models to estimate climate change projections, detailing temperature and precipitation for 2015–2100.

### get_ensemble_forecast
Creates probabilistic forecasts by aggregating data from multiple weather models to quantify uncertainty in immediate climate predictions.

### get_climate_temperature_trend
Calculates the long-term trajectory of average temperatures for a specified geographical area.

## Prompt Examples

**Prompt:** 
```
What will temperatures look like in Paris by 2080 under worst-case emissions?
```

**Response:** 
```
🌡️ **Paris — Climate Projection (SSP5-8.5)**

Current avg summer: 25°C
2050 projected: 28.4°C (+3.4°C)
2080 projected: 31.7°C (+6.7°C)

Under worst-case emissions, Paris summers could resemble present-day Seville by 2080.
```

**Prompt:** 
```
Run an ensemble forecast for London — how confident is the rain prediction?
```

**Response:** 
```
🎲 **London — Ensemble Uncertainty Analysis**

6 models agree on rain tomorrow: ✅ HIGH confidence
Precipitation range: 4-12mm (median: 7mm)
Temperature spread: 14-17°C (tight consensus)

5 of 6 models predict rain > 5mm — high confidence event.
```

**Prompt:** 
```
How much hotter will summers in Dubai get by 2060?
```

**Response:** 
```
🔥 **Dubai — Summer Temperature Trend**

Current avg summer max: 43°C
2040 projected: 44.8°C (+1.8°C)
2060 projected: 46.2°C (+3.2°C)

Extreme heat days (>50°C) projected to increase from 2 to 18 per year by 2060.
```

## Capabilities

### Project long-term climate scenarios
Runs CMIP6 models to predict temperature and precipitation for a given location between 2015 and 2100.

### Calculate multi-model uncertainty ranges
Generates probabilistic forecasts by running multiple weather models together, defining the range of possible outcomes.

### Analyze historical temperature trends
Determines and outputs a long-term trajectory analysis of average temperatures for any specified geographic region.

## Use Cases

### Determining coastal development risk
A developer needs to know if a proposed resort site is viable by 2075. They ask their agent to run `get_climate_projection` for the area, focusing on sea-level rise and temperature thresholds under high-emission scenarios (SSP5-8.5). The resulting data informs whether they need to redesign the entire infrastructure or if the risk is manageable.

### Assessing investment portfolio climate exposure
An ESG analyst needs to quantify risk for a collection of assets across three continents. They use `get_climate_temperature_trend` on each location, comparing the rate of warming against local regulatory thresholds to build an overall corporate sustainability score.

### Planning emergency response logistics
A disaster relief organization needs a quick picture of potential flooding. They run `get_ensemble_forecast` for the region, using the probabilistic range (e.g., 4-12mm) to plan for worst-case rainfall, rather than just relying on median estimates.

### Benchmarking regional climate change
A policy researcher wants to compare how quickly two cities are warming. They run `get_climate_temperature_trend` for both locations and feed the difference into their agent, allowing them to write a comparative paper on differential warming rates.

## Benefits

- Quantify risk using multi-model ensembles. Running `get_ensemble_forecast` doesn't give you a single number; it gives you the range of possibilities, which is what actuaries actually need for proper risk pricing.
- Assess long-term asset viability. Use `get_climate_projection` to determine if property development plans withstand projected temperature and precipitation changes by 2100.
- Track historical context instantly. Instead of manual research, calling `get_climate_temperature_trend` gives you a clear decades-long view of localized warming patterns for any city.
- Support mandatory ESG reporting. The server provides the foundational data needed to model climate impact across entire supply chains and portfolios.
- Handle complex variables. You don't have to juggle multiple academic sources; the MCP framework routes the CMIP6 models directly so your agent can synthesize the findings immediately.

## How It Works

The bottom line is that you get scientifically rigorous climate predictions and uncertainty quantification without having to manually run complex model simulations.

1. Define the parameters: Tell your AI client which location, time frame (e.g., 2050), and scenario type (e.g., SSP5-8.5) are needed.
2. Invoke a specific tool: Your agent calls `get_climate_projection`, `get_ensemble_forecast`, or `get_climate_temperature_trend` with the required inputs.
3. Receive structured data: The server returns quantitative results, including projected averages and confidence ranges, which your AI client uses to formulate an analysis.

## Frequently Asked Questions

**How do I use Open-Meteo Climate & Ensemble MCP Server to compare multiple risk scenarios?**
Use `get_climate_projection` and specify the different SSP emission pathways (e.g., SSP1-2.6 vs SSP5-8.5). This allows your agent to run side-by-side comparisons of temperature and precipitation under vastly different future policy assumptions.

**What is the difference between `get_climate_projection` and `get_climate_temperature_trend`?**
The trend tool calculates a straight line showing how temperatures have changed over decades in the past. The projection tool uses complex climate models to estimate where those trends will go under specific future emission scenarios.

**Does `get_ensemble_forecast` cover long-term changes?**
No, that's a key distinction. `get_ensemble_forecast` is for short-to-medium term probabilistic weather analysis (days/weeks). For long-term climate shifts (decades), use the projection tools.

**Can I get projections for different locations using Open-Meteo Climate & Ensemble MCP Server?**
Yes. Each tool accepts geographic coordinates and location names, letting you run simultaneous, comparative risk assessments across multiple regions in a single workflow.

**What specific emission scenarios can I model using the `get_climate_projection` tool?**
The tool supports multiple Shared Socioeconomic Pathways (SSP) and Representative Concentration Pathways (RCP). You specify the desired SSP code in your request to narrow down the climate risk analysis.

**How does `get_ensemble_forecast` help quantify prediction uncertainty?**
It runs data from six or more separate weather models simultaneously. The output provides a minimum, maximum, and median for metrics like temperature, giving you a clear range of potential outcomes.

**What specific metrics does `get_climate_temperature_trend` return?**
This tool returns time-series data showing projected average temperatures across decades. The results include the absolute value and the calculated change (+X°C) compared to the baseline period.

**Are there usage limits when calling `get_climate_projection` repeatedly?**
Yes, standard marketplace rate limits apply. You can check your current quota on the Vinkius dashboard. For high-volume ESG reporting, we recommend reviewing enterprise connection options.

**Which climate models are used?**
Climate projections use **CMIP6 models** including EC-Earth3P-HR, MRI-AGCM3, and more. Ensemble forecasts combine 6+ operational weather models (ECMWF, GFS, ICON, etc.) for probabilistic uncertainty analysis.