Série de simulations préparatoires à CMIP6, LMDZ-Orchidee, CTRL : NPv5.5

RUN Atlas YEAR Atlas --DJF-- Atlas --JJA-- Zonal mean Dynamical regime Cloud Fraction Diurnal Cycle Scatter Plots Outputs Model Parameters Tested Parameter Period bils rt rst rlut rlutcs crest crelt cret eva pr prw
CLIMATOS 7.097 0.7823 240.4 239.6 269.4 -47.05 29.84 -17.21 3.415 2.61 27.46
NPv5.5 - - - - - - - - - X X Calipso Isccp X X X X Reglage 5.5 1982_1989 0.943 -0.674 239.31 239.984 265.975 -47.9326 25.991 -21.9416 3.07924
MP67 - - - - - - - - - X X Calipso Isccp X X X X Reglage v5.67PDay01 1979_1979 3.4169 1.984 243.59 241.606 265.35 -43.3108 23.744 -19.5668 3.0399
MP72vd2max - - - - - - - - - X X Calipso Isccp X X X X 72vd2, recouvrement max 1979_1979 5.7199 4.4138 242.083 237.669 264.808 -45.5305 27.139 -18.3915 2.98221
MP72vd2 - - - - - - - - - X X Calipso Isccp X X X X 72vd2, recouvrement max-random 1979_1979 5.3585 3.911 241.423 237.512 264.761 -46.199 27.249 -18.95 2.96519
MP72vd2ran - - - - - - - - - X X Calipso Isccp X X X X 72vd2, recouvrement random 1979_1979 4.1352 2.726 240.096 237.37 264.738 -48.2473 27.368 -20.8793 2.95106

Metrics computation

RMSE (Root Mean Square Error) between models and reference datasets (observations or reanalyses depending on the variable; identified at the top of the columns). The RMSE are computed on a 12-month climatological annual cycle (spatio-temporal variability + mean); they mix the information on the spatio-temporal correlation and standard-deviation ratio, as well as the mean bias. The results are shown in % of the average RMSE obtained for the set of simulations chosen as reference. A result of -10 indicates that this error is 10% lower than the average of the errors of the reference simulations.  Basically these metrics provide an overall view on whether the simulations are closer to or further of a set of reference simulations (say, the AMIP simulations, an AR4 simulation...)

Metrics with respect to NPv5.5_1982_1989

Metrics with respect to AR4.00

Metrics with respect to CMIP5/AMIP multi model