@article{fdi:010090064,
  title = {{T}oward machine-assisted tuning avoiding the underestimation of uncertainty in climate change projections},
  author = {{H}ourdin, {F}. and {F}erster, {B}. and {D}eshayes, {J}. and {M}ignot, {J}uliette and {M}usat, {I}. and {W}illiamson, {D}.},
  editor = {},
  language = {{ENG}},
  abstract = {{D}ocumenting the uncertainty of climate change projections is a fundamental objective of the inter-comparison exercises organized to feed into the {I}ntergovernmental {P}anel on {C}limate {C}hange ({IPCC}) reports. {U}sually, each modeling center contributes to these exercises with one or two configurations of its climate model, corresponding to a particular choice of "free parameter" values, resulting from a long and often tedious "model tuning" phase. {H}ow much uncertainty is omitted by this selection and how might readers of {IPCC} reports and users of climate projections be misled by its omission? {W}e show here how recent machine learning approaches can transform the way climate model tuning is approached, opening the way to a simultaneous acceleration of model improvement and parametric uncertainty quantification. {W}e show how an automatic selection of model configurations defined by different values of free parameters can produce different "warming worlds," all consistent with present-day observations of the climate system.},
  keywords = {},
  booktitle = {},
  journal = {{S}cience {A}dvances},
  volume = {9},
  numero = {29},
  pages = {eadf2758 [12 ]},
  ISSN = {2375-2548},
  year = {2023},
  DOI = {10.1126/sciadv.adf2758},
  URL = {https://www.documentation.ird.fr/hor/fdi:010090064},
}