R: Multimodel Ensembles

Ensembles {SailoR}

R Documentation

Multimodel Ensembles

Description

Contains the information of both components of surface wind over the Southern Hemisphere during the period 1979-2015 from the historical forcing experiment of CMIP5 repository. Six ensemble members from the IPSL model, five from the MIROC model and four from the HGEM are included. Additionally, data from the reanalysis ERA5 is also included. All of them have been bilinearly interpolated to a common 1.25º x 1º regular grid.

Usage

data("Ensembles")

Format

A list with 16 data frames:

ERA5: a data frame including the data from reanalysis ERA5.
IPSLr1: first realization of the IPSL model.
IPSLr2: second realization of the IPSL model.
IPSLr3: third realization of the IPSL model.
IPSLr4: fourth realization of the IPSL model.
IPSLr5: fifth realization of the IPSL model.
IPSLr6: sixth realization of the IPSL model.
MIROCr1: first realization of the MIROC model.
MIROCr2: second realization of the MIROC model.
MIROCr3: third realization of the MIROC model.
MIROCr4: fourth realization of the MIROC model.
MIROCr5: fifth realization of the MIROC model.
HGEMr1: first realization of the HadGEM-ES model.
HGEMr2: second realization of the HadGEM-ES model.
HGEMr3: third realization of the HadGEM-ES model.
HGEMr4: fourth realization of the HadGEM-ES model.

Each of these data frames includes 4 variables:

V1: a vector with longitude data.
V2: a vector with latitude data.
V3: zonal component of surface wind (m/s).
V4: meridional component of surface wind (m/s).

References

W. J. Collins, N. Bellouin, M. Doutriaux-Boucher, N. Gedney, P. Halloran, T. Hinton, ... and G. Martin (2011). Development and evaluation of an Earth-System model–HadGEM2. Geosci. Model Dev. Discuss, 4(2), 997-1062.

J. L. Dufresne, M. A. Foujols, S. Denvil, A. Caubel, O. Marti, O. Aumont, ... and S. Bony (2013). Climate change projections using the IPSL-CM5 Earth System Model: from CMIP3 to CMIP5. Climate Dynamics, 40(9-10), 2123-2165.

H. Hersbach, P. de Rosnay, B. Bell, D. Schepers, A. Simmons et al. (2018). Operational global reanalysis: progress, future directions and synergies with NWP. ECMWF Report.

M. Watanabe, T. Suzuki, R. O’ishi, Y. Komuro, S. Watanabe, S. Emori, ... and K. Takata (2010). Improved climate simulation by MIROC5: mean states, variability, and climate sensitivity. Journal of Climate, 23(23), 6312-6335.

Examples

# Load the data
data(Ensembles)

deg2rad<-function(d){
  return(d*pi/180.)
}

# Get area factors from latitudes
Ws<-sqrt(cos(deg2rad(Ensembles$ERA5$V2)))

# Create a ref/ mod object
# ERA5$V3 is zonal component and ERA5$V4 is meridional component
ref<-data.frame("ERA5",Ensembles$ERA5$V3*Ws,Ensembles$ERA5$V4*Ws)
names(ref)<-c("mod","U","V")

# Create an object for the model runs

modA<-data.frame(mod=rep("MIROCr1",nrow(Ensembles$MIROCr1)),
                            U=Ensembles$MIROCr1$V3*Ws,V=Ensembles$MIROCr1$V4*Ws)
modB<-data.frame(mod=rep("MIROC",nrow(Ensembles$MIROCr1)),
                            U=Ensembles$MIROCr1$V3*Ws,V=Ensembles$MIROCr1$V4*Ws)

# In modA every run of the ensemble is considered individually
modA<-rbind(modA,data.frame(mod=rep("MIROCr2",nrow(Ensembles$MIROCr2)),
                 U=Ensembles$MIROCr2$V3*Ws,V=Ensembles$MIROCr2$V4*Ws))
# In modB all the members of the ensemble are taken as a single 
# model run (too long, so that later on, observations will have to be repeated)
modB<-rbind(modB,data.frame(mod=rep("MIROC",nrow(Ensembles$MIROCr2)),
                 U=Ensembles$MIROCr2$V3*Ws,V=Ensembles$MIROCr2$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("MIROCr3",nrow(Ensembles$MIROCr3)),
                            U=Ensembles$MIROCr3$V3*Ws,V=Ensembles$MIROCr3$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("MIROC",nrow(Ensembles$MIROCr3)),
                            U=Ensembles$MIROCr3$V3*Ws,V=Ensembles$MIROCr3$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("MIROCr4",nrow(Ensembles$MIROCr4)),
                            U=Ensembles$MIROCr4$V3*Ws,V=Ensembles$MIROCr4$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("MIROC",nrow(Ensembles$MIROCr4)),
                            U=Ensembles$MIROCr4$V3*Ws,V=Ensembles$MIROCr4$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("MIROCr5",nrow(Ensembles$MIROCr5)),
                            U=Ensembles$MIROCr5$V3*Ws,V=Ensembles$MIROCr5$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("MIROC",nrow(Ensembles$MIROCr5)),
                            U=Ensembles$MIROCr5$V3*Ws,V=Ensembles$MIROCr5$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("IPSLr1",nrow(Ensembles$IPSLr1)),
                            U=Ensembles$IPSLr1$V3*Ws,V=Ensembles$IPSLr1$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("IPSL",nrow(Ensembles$IPSLr1)),
                            U=Ensembles$IPSLr1$V3*Ws,V=Ensembles$IPSLr1$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("IPSLr2",nrow(Ensembles$IPSLr2)),
                            U=Ensembles$IPSLr2$V3*Ws,V=Ensembles$IPSLr2$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("IPSL",nrow(Ensembles$IPSLr2)),
                            U=Ensembles$IPSLr2$V3*Ws,V=Ensembles$IPSLr2$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("IPSLr3",nrow(Ensembles$IPSLr3)),
                            U=Ensembles$IPSLr3$V3*Ws,V=Ensembles$IPSLr3$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("IPSL",nrow(Ensembles$IPSLr3)),
                            U=Ensembles$IPSLr3$V3*Ws,V=Ensembles$IPSLr3$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("IPSLr4",nrow(Ensembles$IPSLr4)),
                            U=Ensembles$IPSLr4$V3*Ws,V=Ensembles$IPSLr4$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("IPSL",nrow(Ensembles$IPSLr4)),
                            U=Ensembles$IPSLr4$V3*Ws,V=Ensembles$IPSLr4$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("IPSLr5",nrow(Ensembles$IPSLr5)),
                            U=Ensembles$IPSLr5$V3*Ws,V=Ensembles$IPSLr5$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("IPSL",nrow(Ensembles$IPSLr5)),
                            U=Ensembles$IPSLr5$V3*Ws,V=Ensembles$IPSLr5$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("IPSLr6",nrow(Ensembles$IPSLr6)),
                            U=Ensembles$IPSLr6$V3*Ws,V=Ensembles$IPSLr6$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("IPSL",nrow(Ensembles$IPSLr6)),
                            U=Ensembles$IPSLr6$V3*Ws,V=Ensembles$IPSLr6$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("HGEMr1",nrow(Ensembles$HGEMr1)),
                            U=Ensembles$HGEMr1$V3*Ws,V=Ensembles$HGEMr1$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("HGEM",nrow(Ensembles$HGEMr1)),
                            U=Ensembles$HGEMr1$V3*Ws,V=Ensembles$HGEMr1$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("HGEMr2",nrow(Ensembles$HGEMr2)),
                            U=Ensembles$HGEMr2$V3*Ws,V=Ensembles$HGEMr2$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("HGEM",nrow(Ensembles$HGEMr2)),
                            U=Ensembles$HGEMr2$V3*Ws,V=Ensembles$HGEMr2$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("HGEMr3",nrow(Ensembles$HGEMr3)),
                            U=Ensembles$HGEMr3$V3*Ws,V=Ensembles$HGEMr3$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("HGEM",nrow(Ensembles$HGEMr3)),
                            U=Ensembles$HGEMr3$V3*Ws,V=Ensembles$HGEMr3$V4*Ws))

modA<-rbind(modA,data.frame(mod=rep("HGEMr4",nrow(Ensembles$HGEMr4)),
                            U=Ensembles$HGEMr4$V3*Ws,V=Ensembles$HGEMr4$V4*Ws))
modB<-rbind(modB,data.frame(mod=rep("HGEM",nrow(Ensembles$HGEMr4)),
                            U=Ensembles$HGEMr4$V3*Ws,V=Ensembles$HGEMr4$V4*Ws))

CMIPA<-list(ref,modA)
names(CMIPA)<-c("ref","mod")
CMIPB<-list(ref,modB)
names(CMIPB)<-c("ref","mod")

#--------------------------------------------------------
# Example 1: Figure 8 (right) from Sáenz et al. (2020)
#--------------------------------------------------------

# Parameters
Uxlim=c(-1,1)
Uylim=c(-0.5,1.5)
Uxlab<-"Ux (m/s)"
Uylab<-"Uy (m/s)"
plotmain<-"CMIP5 samples vs ERA5"
sfactor<-0.2

ref<-CMIPB[["ref"]]
mod<-CMIPB[["mod"]]

sindex<-SailoR.Indices(ref, mod, Ensembles=TRUE)
SailoR.Table(sindex, round_digits = 3)


SailoR.Plot(ref, mod, ColourList=NULL, sfactor, docenter=TRUE,
            Uxlim, Uylim, Uxlab, Uylab, plotmain, plotlegend=TRUE,
            Ensembles=TRUE, plotRMSElegend=TRUE, plotscalelegend=TRUE,
            RMSE_legend_Rounding=2, RMSE_legend_units = " m/s",
            referenceName="ERA5") 

#--------------------------------------------------------
# Example 2: Figure 9 (rigth) from Sáenz et al. (2020)
#--------------------------------------------------------

# Parameters
Uxlim=c(-0.15,0.25)
Uylim=c(0.19,0.61)
Uxlab<-"Ux (m/s)"
Uylab<-"Uy (m/s)"
plotmain<-"CMIP5 samples vs ERA5"
sfactor<-0.01
theColourList<-c("grey40","red","blue","darkolivegreen3",
                 "orange","seagreen3","gold",
                 "purple","pink","magenta",
                 "lightblue","gold4","darkslateblue",
                 "coral","green4","deeppink4")

ref<-CMIPA[["ref"]]
mod<-CMIPA[["mod"]]

sindex<-SailoR.Indices(ref,mod,Ensembles=TRUE)
ST<-SailoR.Table(sindex,round_digits = 1)

p<-SailoR.Plot(ref, mod, ColourList=theColourList, sfactor, docenter=FALSE,
            Uxlim, Uylim, Uxlab, Uylab, plotmain, plotlegend=FALSE,
            Ensembles=TRUE, plotRMSElegend=FALSE, plotscalelegend=TRUE,
            RMSE_legend_Rounding=2, RMSE_legend_units = " m/s",
            referenceName="ERA5")  

#legend
legend(x="topright",
       as.character(ST$modelName),
       col=theColourList,lwd=2,
       lty=c("dashed",rep("solid",15)),cex=1,ncol=2)

#RMSElegend
legend(x="bottomright",
       paste(as.character(unique(mod$mod)),": ",ST$RMSE[-1],"m/s",sep=""),
       col=theColourList,
       pch=16,lwd=3,
       title=expression(bold("RMSE")),
       cex=0.9,ncol=3)

[Package SailoR version 1.2 Index]