# Climate Projections & Environmental Scraper (Open-Meteo) (`ninhothedev/climate-projections-scraper`) Actor

$0.5/1K 🔥 Climate Projections! Get downscaled climate model forecasts for any location. No key. JSON, CSV, Excel or API in seconds. Plan for climate risk and resilience ⚡

- **URL**: https://apify.com/ninhothedev/climate-projections-scraper.md
- **Developed by:** [ninhothedev](https://apify.com/ninhothedev) (community)
- **Categories:** Developer tools, Automation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Climate Projections & Environmental Scraper (Open-Meteo)

Scrape **long-term climate projections (CMIP6 models to 2050)** and **ensemble
forecast spread** from the free [Open-Meteo](https://open-meteo.com) APIs — **no
API key required**. Every response is un-pivoted from Open-Meteo's columnar arrays
into clean, flat, one-row-per-timestep records ready for analysis.

Typical cost is roughly **~$0.5 per 1,000 results** on Apify compute — you only
pay for platform usage, the data source itself is free.

***

### What this actor does (and how it differs from our short-range weather actors)

Open-Meteo exposes several distinct APIs. This actor deliberately covers the
**long-horizon / probabilistic** endpoints that our other actors do **not**:

| Actor | Endpoint | Horizon | This one? |
|-------|----------|---------|-----------|
| **This actor** | `climate-api` (CMIP6) + `ensemble-api` | **Decades → 2050**, and ensemble member spread | Yes |
| [weather-scraper](https://apify.com/ninhothedev/weather-scraper) | forecast API | Short-range daily/hourly forecast | No — different actor |
| [marine-weather-scraper](https://apify.com/ninhothedev/marine-weather-scraper) | marine API | Short-range waves/sea state | No — different actor |
| [flood-forecast-scraper](https://apify.com/ninhothedev/flood-forecast-scraper) | flood API | River discharge forecast | No — different actor |
| air-quality-scraper | air-quality API | Short-range pollutants | No — different actor |
| [nasa-power-scraper](https://apify.com/ninhothedev/nasa-power-scraper) | NASA POWER | Historical solar/meteo | No — different source |

**In one line:** the weather / marine / flood / air-quality actors answer *"what
will the weather be in the next few days?"* — **this actor answers *"what does the
climate look like out to 2050, and how much do the ensemble members disagree?"***

***

### Modes

#### 1. `projections` — CMIP6 climate models to 2050

Daily downscaled climate-model output per model. CMIP6 (Coupled Model
Intercomparison Project Phase 6) high-resolution models simulate temperature,
precipitation and more under future scenarios, letting you compare how different
models project a location's climate decades ahead.

**Verified high-resolution models:**
`CMCC_CM2_VHR4`, `FGOALS_f3_H`, `HiRAM_SIT_HR`, `MRI_AGCM3_2_S`,
`EC_Earth3P_HR`, `MPI_ESM1_2_XR`, `NICAM16_8S`.
Pass several to compare model spread — each produces its own rows.

#### 2. `ensemble` — forecast member spread

Ensemble forecasts run the same model many times with slightly perturbed initial
conditions. The **spread** between members is a measure of forecast uncertainty:
tight spread = high confidence, wide spread = low confidence. This actor computes
`member_min / member_max / member_mean / member_spread` (null-safe) per timestep.
`icon_seamless` returns ~39 members, `gfs_seamless` ~30.

***

### Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `mode` | select | `projections` | `projections` or `ensemble`. |
| `points` | array | `["52.5200,13.4050","40.7128,-74.0060"]` | Points as `"lat,lon"` strings. |
| `models` | array | `["EC_Earth3P_HR"]` (proj) / `["icon_seamless"]` (ens) | Model IDs (see lists above). |
| `dailyVariables` | array | `["temperature_2m_max","temperature_2m_min","precipitation_sum"]` | Projections only; each becomes a column. |
| `hourlyVariable` | string | `temperature_2m` | Ensemble only; the base variable whose spread is computed. |
| `startDate` | string | `2020-01-01` | Projections only (from 1950). |
| `endDate` | string | `2050-12-31` | Projections only (to 2050). |
| `maxItems` | integer | `2000` (max `100000`) | Hard cap on output rows. |

### Output

**Projections** (`type: "climate_projection"`):

```json
{
  "type": "climate_projection",
  "latitude": 52.52, "longitude": 13.405,
  "model": "EC_Earth3P_HR",
  "date": "2020-01-01",
  "temperature_max_c": 11.7,
  "temperature_min_c": 8.0,
  "precipitation_mm": 4.02,
  "units": {"time": "iso8601", "temperature_2m_max": "°C", "precipitation_sum": "mm"},
  "source": "open-meteo-climate",
  "scraped_at": "2026-08-11T04:00:00Z"
}
```

Any extra requested `dailyVariables` are added as their own keys. Null values are
kept null — never fabricated.

**Ensemble** (`type: "ensemble"`):

```json
{
  "type": "ensemble",
  "latitude": 52.52, "longitude": 13.405,
  "model": "icon_seamless",
  "time": "2026-08-11T00:00",
  "variable": "temperature_2m",
  "member_count": 39,
  "member_min": 15.5, "member_max": 17.2,
  "member_mean": 16.41, "member_spread": 1.7,
  "members": [15.5, 16.1, "..."],
  "source": "open-meteo-ensemble",
  "scraped_at": "2026-08-11T04:00:00Z"
}
```

### Use cases

- **Climate risk analysis** — quantify future heat, cold and precipitation extremes.
- **Long-term planning** — infrastructure, energy and water siting decisions to 2050.
- **Agriculture** — projected growing-season temperature and rainfall shifts.
- **Insurance modelling** — feed CMIP6 spread and ensemble uncertainty into risk models.

### Related actors

- [weather-scraper](https://apify.com/ninhothedev/weather-scraper)
- [nasa-power-scraper](https://apify.com/ninhothedev/nasa-power-scraper)
- [flood-forecast-scraper](https://apify.com/ninhothedev/flood-forecast-scraper)
- [marine-weather-scraper](https://apify.com/ninhothedev/marine-weather-scraper)

### Notes & attribution

Data © [Open-Meteo.com](https://open-meteo.com), CC-BY 4.0. Free for non-commercial
and commercial use within their fair-use limits; no API key needed.

# Actor input Schema

## `mode` (type: `string`):

Which Open-Meteo dataset to scrape. 'projections' pulls long-term CMIP6 climate model data (daily, to 2050) per model. 'ensemble' pulls short-range ensemble forecasts and returns the member spread (min/max/mean) per timestep.

## `points` (type: `array`):

List of geographic points to query, each as a 'latitude,longitude' string (decimal degrees), e.g. '52.5200,13.4050'. Invalid entries are skipped.

## `models` (type: `array`):

Model IDs to query. PROJECTIONS (CMIP6 high-resolution, verified): CMCC\_CM2\_VHR4, FGOALS\_f3\_H, HiRAM\_SIT\_HR, MRI\_AGCM3\_2\_S, EC\_Earth3P\_HR, MPI\_ESM1\_2\_XR, NICAM16\_8S. ENSEMBLE: icon\_seamless (~39 members), gfs\_seamless (~30 members), gfs025, icon\_d2, bom\_access\_global\_ensemble, gem\_global\_ensemble. Multiple models produce separate rows per model.

## `dailyVariables` (type: `array`):

PROJECTIONS mode only. Daily CMIP6 variables to request, e.g. temperature\_2m\_max, temperature\_2m\_min, precipitation\_sum, temperature\_2m\_mean, windspeed\_10m\_max, shortwave\_radiation\_sum, relative\_humidity\_2m\_max. Each requested variable becomes its own column in the output rows.

## `hourlyVariable` (type: `string`):

ENSEMBLE mode only. The single base hourly variable whose per-member spread is computed, e.g. temperature\_2m, precipitation, wind\_speed\_10m, pressure\_msl.

## `startDate` (type: `string`):

PROJECTIONS mode only. ISO start date (YYYY-MM-DD). CMIP6 data is available from 1950 onward.

## `endDate` (type: `string`):

PROJECTIONS mode only. ISO end date (YYYY-MM-DD). CMIP6 projections extend to 2050-12-31.

## `maxItems` (type: `integer`):

Hard cap on the total number of output rows across all points and models. Stops early once reached.

## Actor input object example

```json
{
  "mode": "projections",
  "points": [
    "52.5200,13.4050",
    "40.7128,-74.0060"
  ],
  "models": [
    "EC_Earth3P_HR"
  ],
  "dailyVariables": [
    "temperature_2m_max",
    "temperature_2m_min",
    "precipitation_sum"
  ],
  "hourlyVariable": "temperature_2m",
  "startDate": "2020-01-01",
  "endDate": "2050-12-31",
  "maxItems": 2000
}
```

# Actor output Schema

## `records` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "points": [
        "52.5200,13.4050",
        "40.7128,-74.0060"
    ],
    "models": [
        "EC_Earth3P_HR"
    ],
    "dailyVariables": [
        "temperature_2m_max",
        "temperature_2m_min",
        "precipitation_sum"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("ninhothedev/climate-projections-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "points": [
        "52.5200,13.4050",
        "40.7128,-74.0060",
    ],
    "models": ["EC_Earth3P_HR"],
    "dailyVariables": [
        "temperature_2m_max",
        "temperature_2m_min",
        "precipitation_sum",
    ],
}

# Run the Actor and wait for it to finish
run = client.actor("ninhothedev/climate-projections-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "points": [
    "52.5200,13.4050",
    "40.7128,-74.0060"
  ],
  "models": [
    "EC_Earth3P_HR"
  ],
  "dailyVariables": [
    "temperature_2m_max",
    "temperature_2m_min",
    "precipitation_sum"
  ]
}' |
apify call ninhothedev/climate-projections-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,ninhothedev/climate-projections-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/yrwRY2FdvbaD0Qi4S/builds/t48hfDWxjdyaD6Rzh/openapi.json