@article{fdi:010079126,
  title = {{T}owards an objective assessment of climate multi-model ensembles - a case study : the {S}enegalo-{M}auritanian upwelling region},
  author = {{M}ignot, {J}uliette and {M}ejia, {C}. and {S}orror, {C}. and {S}ylla, {A}. and {C}repon, {M}. and {T}hiria, {S}.},
  editor = {},
  language = {{ENG}},
  abstract = {{C}limate simulations require very complex numerical models. {U}nfortunately, they typically present biases due to parameterizations, choices of numerical schemes, and the complexity of many physical processes. {B}eyond improving the models themselves, a way to improve the performance of the modeled climate is to consider multi-model combinations. {I}n the present study, we propose a method to select the models that yield a multi-model ensemble combination that efficiently reproduces target features of the observations. {W}e used a neural classifier (self-organizing maps), associated with a multi-correspondence analysis to identify the models that best represent some target climate property. {W}e can thereby determine an efficient multi-model ensemble. {W}e illustrated the methodology with results focusing on the mean sea surface temperature seasonal cycle in the {S}enegalo-{M}auritanian region. {W}e compared 47 {CMIP}5 model configurations to available observations. {T}he method allows us to identify a subset of {CMIP}5 models able to form an efficient multi-model ensemble. {T}he future decrease in the {S}enegalo-{M}auritanian upwelling proposed in recent studies is then revisited using this multi-model selection.},
  keywords = {{SENEGAL} ; {MAURITANIE} ; {ATLANTIQUE}},
  booktitle = {},
  journal = {{G}eoscientific {M}odel {D}evelopment},
  volume = {13},
  numero = {6},
  pages = {2723--2742},
  ISSN = {1991-959{X}},
  year = {2020},
  DOI = {10.5194/gmd-13-2723-2020},
  URL = {https://www.documentation.ird.fr/hor/fdi:010079126},
}