The CEDA data browser mentioned here is not showing all of the data that is available in the archive. To provide a more complete view of what is available and make it easier to search and download data, we now have a Python script that will provide a list of all available models/experiments/variables and can download specified subsets of experiments and variables. The script can be accessed through github at: https://github.com/ccmi1-test/PythonScripts. Note that Python version 3.3 or later is needed.
In addition to downloading data for a specified combination of experiments and variables, the script will also produce a listing of the archive when it is run. An example of the listing that is produced can be found here.
If you have any problems with the script or, even better, suggestions for improving it, let us know.
The CEDA data archive:
The output from participating models is stored at the Centre for Environmental Data Analysis (CEDA) in the United Kingdom. If you had an account with the British Atmospheric Data Centre (BADC) there is a good chance that account will work for CEDA. If you need an account you can apply for one at https://help.ceda.ac.uk/article/81-registering
To apply to access to the CCMI-2022 archive, visit the catalogue entry for one of the published CCMI-2022 dataset and click on ‘Request Access’. Applying for access to any of the CCMI-2022 model datasets will grant access to all of the model datasets. For example, you can access the entry for NIWA-UKCA2 refD1 output here. You can also apply for access by going directly to the CCMI-2022 access registration page.
As part of the request for access you will need to agree to a ‘Restricted Use General Licence’, which is meant to apply to the use of CCMI-2022 during the Phase 1 access period. More details on the CCMI-2022 data policy, including the length of time Phase 1 restrictions will be effect, can be found here.
During the registration process you will be asked for a short description of the intended use of the model data. To reduce the chances of duplication, and as applicable, we will summarize the proposed studies that are received below.
Note that a simple way to have an up-to-the-minute view of what data is currently available in the archive is through the CEDA data browser, with the top level of the CCMI-2022 archive at https://data.ceda.ac.uk/badc/ccmi/data/post-cmip6/ccmi-2022.
Contact information for each participating model
Participating Modelling Groups and Contact Information
|EMAC||MESSy Consortium||Andreas Pfeiffer|