Articles | Volume 11, issue 3
https://doi.org/10.5194/esd-11-807-2020
https://doi.org/10.5194/esd-11-807-2020
Research article
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16 Sep 2020
Research article | Highlight paper |  | 16 Sep 2020

An investigation of weighting schemes suitable for incorporating large ensembles into multi-model ensembles

Anna Louise Merrifield, Lukas Brunner, Ruth Lorenz, Iselin Medhaug, and Reto Knutti

Data sets

ESD_weighting_large_ensembles: Paper Release (Version v1.0) A. L. Merrifield, L. Brunner, and R. Lorenz https://doi.org/10.5281/zenodo.4028924

Model code and software

WCRP Coupled Model Intercomparison Project (Phase 5) ESGF https://esgf-node.llnl.gov/projects/cmip5/

CanESM2 Large Ensembles Output Environment and Climate Change Canada https://open.canada.ca/data/en/dataset/aa7b6823-fd1e-49ff-a6fb-68076a4a477c

MPI Grand Ensemble Output Max Planck Institute for Meteorology https://esgf-data.dkrz.de/projects/mpi-ge/

ERA-20C Output ECMWF https://apps.ecmwf.int/datasets/data/era20c-moda/levtype=sfc/type=an/

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Short summary
Justifiable uncertainty estimates of future change in northern European winter and Mediterranean summer temperature can be obtained by weighting a multi-model ensemble comprised of projections from different climate models and multiple projections from the same climate model. Weights reduce the influence of model biases and handle dependence by identifying a projection's model of origin from historical characteristics; contributions from the same model are scaled by the number of members.
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