Série de simulations préparatoires à CMIP6, LMDZ-Orchidee, CTRL : tuningVLR.00


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
tuningVLR.00 - - - - - - - - - X X Calipso Isccp X X X X CTRL 2390_2399 0.176 0.143 235.504 235.361 266.942 -53.1401 31.581 -21.5591 2.64247
tuningVLR.01 G - - - - - - - - X X Calipso Isccp X X X X CLDLC=8.16 2390_2399 -6.9142 -6.976 227.204 234.18 266.763 -61.4123 32.583 -28.8293 2.65555
tuningVLR.02 - - - - - - - - - X X Calipso Isccp X X X X CLDLC=3.16 2390_2399 2.7374 2.716 238.501 235.785 267.038 -50.193 31.253 -18.94 2.63279
tuningVLR.03 - - - - - - - - - X X Calipso Isccp X X X X CLDLC=2.5 2390_2399 4.544 4.513 240.658 236.145 267.104 -48.0641 30.959 -17.1051 2.62641

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 tuningVLR.00_2390_2399

Metrics with respect to AR4.00

Metrics with respect to CMIP5/AMIP multi model