Automated Author ProfileHourdin, Frédéric
LMD-IPSL
Hourdin, Frédéric
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.4 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
The dataset contains pre-processed multi-annual mean 2D maps of surface temperature, rainfall and energy fluxes computed from ensembles of climate simulations. The ensembles consist in : 1) CMIP5 and CMIP6 multi model ensembles 2) perturbed physics ensembles run with the LMDZ global atmospheric model and IPSL coupled model during an automated tuning procedure. All the dataset are provided on the original model grid, and scripts are provided to interpolate the dataset on a 120x90 global regular grid. The interpolates dataset and scripts were used to compute evaluation metrics for a publication in "Science Advances" in 2023: "Towards machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections" by Frédéric Hourdin, Brady Ferster, Julie Deshayes, Juliette Mignot, Ionela Musat, Daniel Williamson and in the associated Supplementary information.
Authors
- Hourdin, Frédéric