Download daily climate scenarios¶

To conduct future suitability comparisons, it is necessary to obtain climate scenario data. This example demonstrates how to download climate variables from the NEX-GDDP-CMIP6 project, which provides statistically downscaled daily climate projections for the CMIP6 experiment across “Tier 1” SSP emission scenarios.

Dataset Version

The code supports Version 2 (v2) of the dataset. If a specific product is not yet available in v2, the tool automatically falls back to the previous version

Python API Usage¶

You can download specific scenarios by defining SSPs, global climate models (GCMs), time periods, and meteorological variables directly in Python.

1. Define the Query¶

First, initialize the downloader and set your query parameters:

Python

from datasets.download_data import DownloadCMIP6Data

yearsofinterest = list(range(2021, 2100))
cmip_dowloader = DownloadCMIP6Data(url_root = config.CLIMATE.url_root)


cmip_dowloader.set_query(
    years=yearsofinterest,
    gcms=['ACCESS-ESM1-5', 'MPI-ESM1-2-HR'],
    ssps=['ssp585'],
    variables=['pr', 'rsds']
)

print(cmip_dowloader.products_to_download)

2. Download a Product¶

You can iterate through the products_to_download list to download specific files:

Python

from pathlib import Path

# Select a single product from the list (example: index 0)
id_product = 0
gcm, ssp, var, year = list(cmip_dowloader.products_to_download)[id_product]
url_dir, fn = cmip_dowloader.find_remote_file(gcm, ssp, var, year)

# Path where the data will be saved
output_path = Path(output_dir) / var / ssp / gcm
# Start the download
dwnd_path = cmip_dowloader.download_data(url_dir, fn, output_path)

3. Preprocessing and Clipping¶

Raw downloaded files cover the global domain. You can clip the dataset to a specific geographic extent and convert units using the ProcessTools class.

Meteorological variables also require unit conversion:

Temperature variables (tas, tasmin, tasmax) are provided in Kelvin.
Precipitation (pr) requires multiplication by 86,400 to convert from kg m⁻² s⁻¹ to mm/day.

Python

from datasets.download_data import ProcessTools
xrdata_processor = ProcessTools()

clip_extent = [-26, -37, 56, 40]  # min lon, min lat, max lon, max lat
xrdata = xrdata_processor.clip_spdata(
    clip_extent,
    xrdata_path=dwnd_path,
    rotate=True,
    chunks='auto'
)


# Preprocess (Unit conversion)
xrdata = xrdata_processor.preprocess_xrdata(xrdata)

# Export the processed dataset as a NetCDF file
xrdata_processor.save_asnc(xrdata, fn=dwnd_path)

Command-Line Usage¶

Downloading can be fully automated using a YAML configuration file.

1. Configure the YAML¶

The file download_cmip6.yaml (located in yaml_configuration/) allows users to specify:

YAML

GENERAL_INFO:
  output_dir: '../nex-gddp-cmip6_AFRICA/'  # directory where data will be downloaded
  variables: ['pr', 'tasmin', 'tasmax', 'rsds']  # meteorological variables
  years: [2021, 2100]  # period of interest
  clip_extent: [-26, -37, 56, 40]  # Africa extent
  nworkers: 16  # maximum number of download workers

CLIMATE:
  gcms: ['ACCESS-ESM1-5', 'MPI-ESM1-2-HR', 'EC-Earth3', 'INM-CM5-0', 'MRI-ESM2-0'] # climate models
  ssps: ['ssp245', 'ssp126', 'ssp585']  # emission scenarios
  url_root: 'https://nex-gddp-cmip6.s3-us-west-2.amazonaws.com/NEX-GDDP-CMIP6'

2. Download the data¶

Once the configuration file is ready, run the download script:

Bash

python datasets/download_data.py -config yaml_configuration/download_cmip6.yaml