SailoR
An Extension of the Taylor Diagram to Two-Dimensional Vector Data

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Ensembles: Multimodel Ensembles

Ensembles: Multimodel Ensembles
In SailoR: An Extension of the Taylor Diagram to Two-Dimensional Vector Data

Description Usage Format References Examples

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

1
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

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# 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)

SailoR documentation built on Oct. 23, 2020, 7:46 p.m.

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