@article{fdi:010035736,
  title = {{P}rojection of future climate change conditions using {IPCC} simulations, neural networks and {B}ayesian statistics. {P}art 1: {T}emperature mean state and seasonal cycle in {S}outh {A}merica},
  author = {{B}oulanger, {J}ean-{P}hilippe and {M}artinez, {F}. and {S}egura, {E}.{C}.},
  editor = {},
  language = {{ENG}},
  abstract = {{P}rojections for {S}outh {A}merica of future climate change conditions in mean state and seasonal cycle for temperature during the twenty-first century are discussed. {O}ur analysis includes one simulation of seven {A}tmospheric-{O}cean {G}lobal {C}irculation {M}odels, which participated in the {I}ntergovernmental {P}anel on {C}limate {C}hange {P}roject and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three {S}pecial {R}eport on {E}missions {S}cenarios ({SRES}) {A}2, {A}1{B}, and {B}1. {W}e developed a statistical method based on neural networks and {B}ayesian statistics to evaluate the models' skills in simulating late twentieth century temperature over continental areas. {S}ome criteria [model weight indices ({MWI}s)] are computed allowing comparing over such large regions how each model captures the temperature large scale structures and contributes to the multi-model combination. {A}s the study demonstrates, the use of neural networks, optimized by {B}ayesian statistics, leads to two major results. {F}irst, the {MWI}s can be interpreted as optimal weights for a linear combination of the climate models. {S}econd, the comparison between the neural network projection of twenty-first century conditions and a linear combination of such conditions allows the identification of the regions, which will most probably change, according to model biases and model ensemble variance. {M}odel simulations in the southern tip of {S}outh {A}merica and along the {C}hilean and {P}eruvian coasts or in the northern coasts of {S}outh {A}merica ({V}enezuela, {G}uiana) are particularly poor. {O}verall, our results present an upper bound of potential temperature warming for each scenario. {S}patially, in {SRES} {A}2, our major findings are that {T}ropical {S}outh {A}merica could warm up by about 4 degrees {C}, while southern {S}outh {A}merica ({SSA}) would also undergo a near 2-3 degrees {C} average warming. {I}nterestingly, this annual mean temperature trend is modulated by the seasonal cycle in a contrasted way according to the regions. {I}n {SSA}, the amplitude of the seasonal cycle tends to increase, while in northern {S}outh {A}merica, the amplitude of the seasonal cycle would be reduced leading to much milder winters. {W}e show that all the scenarios have similar patterns and only differ in amplitude. {SRES} {A}1{B} differ from {SRES} {A}2 mainly for the late twenty-first century, reaching more or less an 80-90% amplitude compared to {SRES} {A}2. {SRES} 131, however, diverges from the other scenarios as soon as 2025. {F}or the late twenty-first century, {SRES} {B}1 displays amplitudes, which are about half those of {SRES} {A}2.},
  keywords = {},
  booktitle = {},
  journal = {{C}limate {D}ynamics},
  volume = {27},
  numero = {2-3},
  pages = {233--259},
  ISSN = {0930-7575},
  year = {2006},
  DOI = {10.1007/s00382-006-0134-8},
  URL = {https://www.documentation.ird.fr/hor/fdi:010035736},
}