@article{fdi:010081271,
  title = {{S}trong regional influence of climatic forcing datasets on global crop model ensembles},
  author = {{R}uane, {A}. {C}. and {P}hillips, {M}. and {M}uller, {C}. and {E}lliott, {J}. and {J}agermeyr, {J}. and {A}rneth, {A}. and {B}alkovic, {J}. and {D}eryng, {D}. and {F}olberth, {C}. and {I}izumi, {T}. and {I}zaurralde, {R}. {C}. and {K}habarov, {N}. and {L}awrence, {P}. and {L}iu, {W}. {F}. and {O}lin, {S}. and {P}ugh, {T}. {A}. {M}. and {R}osenzweig, {C}. and {S}akurai, {G}. and {S}chmid, {E}. and {S}ultan, {B}enjamin and {W}ang, {X}. {H}. and de {W}it, {A}. and {Y}ang, {H}.},
  editor = {},
  language = {{ENG}},
  abstract = {{W}e present results from the {A}gricultural {M}odel {I}ntercomparison and {I}mprovement {P}roject ({A}g{MIP}) {G}lobal {G}ridded {C}rop {M}odel {I}ntercomparison ({GGCMI}) {P}hase {I}, which aligned 14 global gridded crop models ({GGCM}s) and 11 climatic forcing datasets ({CFD}s) in order to understand how the selection of climate data affects simulated historical crop productivity of maize, wheat, rice and soybean. {R}esults show that {CFD}s demonstrate mean biases and differences in the probability of extreme events, with larger uncertainty around extreme precipitation and in regions where observational data for climate and crop systems are scarce. {C}ountries where simulations correlate highly with reported {FAO} national production anomalies tend to have high correlations across most {CFD}s, whose influence we isolate using multi-{GGCM} ensembles for each {CFD}. {C}orrelations compare favorably with the climate signal detected in other studies, although production in many countries is not primarily climate-limited (particularly for rice). {B}ias-adjusted {CFD}s most often were among the highest model-observation correlations, although all {CFD}s produced the highest correlation in at least one top-producing country. {A}nalysis of larger multi-{CFD}-multi-{GGCM} ensembles (up to 91 members) shows benefits over the use of smaller subset of models in some regions and farming systems, although bigger is not always better. {O}ur analysis suggests that global assessments should prioritize ensembles based on multiple crop models over multiple {CFD}s as long as a top-performing {CFD} is utilized for the focus region.},
  keywords = {{A}gricultural {M}odel {I}ntercomparison and {I}mprovement {P}roject ({A}g{MIP}) ; {G}lobal {G}ridded {C}rop {M}odel {I}ntercomparison ({GGCMI}) ; {C}limatic {F}orcing ; {D}atasets ; {C}limate {I}mpacts ; {A}groclimate ; {C}rop production},
  booktitle = {},
  journal = {{A}gricultural and {F}orest {M}eteorology},
  volume = {300},
  numero = {},
  pages = {108313 [18 p.]},
  ISSN = {0168-1923},
  year = {2021},
  DOI = {10.1016/j.agrformet.2020.108313},
  URL = {https://www.documentation.ird.fr/hor/fdi:010081271},
}