Co-leads: Martin Juckes, STFC and Chloe Mackallah, CSIRO
This task team will focus on how the CMIP data request should define the quantities from CMIP7 simulations that should be archived, both quantities of general interest needed from the majority of CMIP7 model intercomparsion projects (MIPs) and more specialised quantities only of interest to individual/small numbers of MIPs
Challenge
The complexity of the data request has increased from the early days of model intercomparisons, as has the data volume. The CMIP6 request defined distinct sets of highly tailored variables to be saved from each of the more than 200 experiments. Following review of the CMIP6 experience, some rationalisation has been agreed and there will be more consistency of the request between related experiments, while allowing enough flexibility to avoid archiving redundant data. Further details in Juckes et al. (2020).
It has also been suggested that the model used for defining the priority of variables should be simplified, with greater use of common template variable lists for experiments and a more cautious approach to applying top priority to data requirements.
Aim & Objectives
The aim of this TT is to ensure that a consolidated final request is ready when modelling centres need to start CMIP7 simulations.
The objectives are to:
- Oversee the creation of the CMIP7 Data Request which should provide participating modelling centres with full details of CMIP7 output requirements from participating science teams.
- Ensure that the CMIP7 output requirements accurately reflect both the ambition of the science teams, the constraints of the data managing services, and the resources of the modelling centres.
A number of strategic requirements have been identified for the next version of the data request (see IS-ENES3 Milestone M10.2 report). These include:
- maintaining a similar structure, to minimise effort needed to interpret the Request
- clearer links with externals sources of information such as the controlled vocabularies, allowing for alternate representations such as normalised databases, and;
- the ability to clearly understand changes to the content of the Data Request as it is updated.
Members
Climate Data Request Task Team members
| Martin Juckes | 2022- | Co-lead | STFC | UK |
| Chloe Mackallah | 2022- | Co-lead | CSIRO | Australia |
| James Anstey | 2022- | Member | Environment Canada | Canada |
| Tommi Bergman | 2022- | Member | FMI | Finland |
| Léa Braschi | 2022- | Member | CBCL | Canada |
| Pierre-Antoine Bretonnière | 2022- | Member | BSC-CNS | Spain |
| Antonio S. Cofiño | 2022- | Member | IFCA-CSIC | Spain |
| Michio Kawamiya | 2022- | Member | JAMSTEC | Japan |
| Hyungjun Kim | 2022- | Member | KAIST CEE | Republic of Korea |
| Charles Koven | 2022- | Member | Berkeley Lab | USA |
| Hsin-Chien Liang | 2022- | Member | Sinica | Taiwan |
| Tomas Lovato | 2022- | Member | CMCC | Italy |
| Marianne Madsen | 2022- | Member | DMI | Denmark |
| Marie-Pierre Moine | 2022- | Member | CERFACS | France |
| Alison Pamment | 2022- | Member | STFC | UK |
| Charlotte Pascoe | 2022- | Member | STFC | UK |
| Gaëlle Rigoudy | 2022- | Member | Météo France | France |
| Martin Schupfner | 2022- | Member | DKRZ | Germany |
| Klaus Zimmermann | 2022- | Member | SMHI | Sweden |
Activities
Open call for members closed in October 2022 – call text available here.
